RFComparisonsData.csv

paper_title,author,year,journal,dataset,dataset_size,num_inputs,classes,train_size,test_size,method,situation,error,error_sd,tuning,ntree,mtry,evaluation,comparison
Random forests,Breiman,2001,Machine Learning,glass,214,9,6,192,22,rf,allround,21.2,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,breast cancer,699,9,2,629,70,rf,allround,2.7,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,pima,768,8,2,691,77,rf,allround,24.3,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,sonar,208,60,2,187,21,rf,allround,18,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vowel,990,10,11,891,99,rf,allround,3.3,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ionosphere,351,34,2,315,36,rf,allround,7.5,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vehicle,846,18,4,761,85,rf,allround,26.4,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,german credit,1000,24,2,900,100,rf,allround,26.2,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,image,2310,19,7,2079,231,rf,allround,2.7,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ecoli,336,7,8,302,34,rf,allround,13,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,votes,435,16,2,391,44,rf,allround,4.6,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,liver,345,6,2,310,35,rf,allround,24.7,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,letters,20000,16,26,15000,5000,rf,allround,4.7,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,satellite,6435,36,6,4435,2000,rf,allround,10.5,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,zip,9298,256,10,7291,2007,rf,allround,7.8,NA,none,200,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,waveform,3300,21,3,300,3000,rf,allround,17.3,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,twonorm,3300,20,2,300,3000,rf,allround,3.9,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,threenorm,3300,20,2,300,3000,rf,allround,17.5,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ringnorm,3300,20,2,300,3000,rf,allround,4.9,NA,none,100,1,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,glass,214,9,6,192,22,rf,allround,20.6,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,breast cancer,699,9,2,629,70,rf,allround,2.9,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,pima,768,8,2,691,77,rf,allround,24.2,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,sonar,208,60,2,187,21,rf,allround,15.9,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vowel,990,10,11,891,99,rf,allround,3.4,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ionosphere,351,34,2,315,36,rf,allround,7.1,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vehicle,846,18,4,761,85,rf,allround,25.8,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,german credit,1000,24,2,900,100,rf,allround,24.4,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,image,2310,19,7,2079,231,rf,allround,2.1,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ecoli,336,7,8,302,34,rf,allround,12.8,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,votes,435,16,2,391,44,rf,allround,4.1,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,liver,345,6,2,310,35,rf,allround,25.1,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,letters,20000,16,26,15000,5000,rf,allround,3.5,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,satellite,6435,36,6,4435,2000,rf,allround,8.6,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,zip,9298,256,10,7291,2007,rf,allround,6.3,NA,default,200,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,waveform,3300,21,3,300,3000,rf,allround,17.2,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,twonorm,3300,20,2,300,3000,rf,allround,3.9,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,threenorm,3300,20,2,300,3000,rf,allround,17.5,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ringnorm,3300,20,2,300,3000,rf,allround,4.9,NA,default,100,log2(p+1),50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,glass,214,9,6,192,22,adaboost,allround,22,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,breast cancer,699,9,2,629,70,adaboost,allround,3.2,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,pima,768,8,2,691,77,adaboost,allround,26.6,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,sonar,208,60,2,187,21,adaboost,allround,15.6,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vowel,990,10,11,891,99,adaboost,allround,4.1,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ionosphere,351,34,2,315,36,adaboost,allround,6.4,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vehicle,846,18,4,761,85,adaboost,allround,23.2,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,german credit,1000,24,2,900,100,adaboost,allround,23.5,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,image,2310,19,7,2079,231,adaboost,allround,1.6,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ecoli,336,7,8,302,34,adaboost,allround,14.8,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,votes,435,16,2,391,44,adaboost,allround,4.8,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,liver,345,6,2,310,35,adaboost,allround,30.7,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,letters,20000,16,26,15000,5000,adaboost,allround,3.4,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,satellite,6435,36,6,4435,2000,adaboost,allround,8.8,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,zip,9298,256,10,7291,2007,adaboost,allround,6.2,NA,none,100,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,waveform,3300,21,3,300,3000,adaboost,allround,17.8,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,twonorm,3300,20,2,300,3000,adaboost,allround,4.9,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,threenorm,3300,20,2,300,3000,adaboost,allround,18.8,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ringnorm,3300,20,2,300,3000,adaboost,allround,6.9,NA,none,50,NA,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,glass,214,9,6,192,22,orf-rnd,allround,24.4,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,breast cancer,699,9,2,629,70,orf-rnd,allround,3.1,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,pima,768,8,2,691,77,orf-rnd,allround,23,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,sonar,208,60,2,187,21,orf-rnd,allround,13.6,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vowel,990,10,11,891,99,orf-rnd,allround,3.3,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ionosphere,351,34,2,315,36,orf-rnd,allround,5.5,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vehicle,846,18,4,761,85,orf-rnd,allround,23.1,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,german credit,1000,24,2,900,100,orf-rnd,allround,22.8,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,image,2310,19,7,2079,231,orf-rnd,allround,1.6,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ecoli,336,7,8,302,34,orf-rnd,allround,12.9,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,votes,435,16,2,391,44,orf-rnd,allround,4.1,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,liver,345,6,2,310,35,orf-rnd,allround,27.3,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,letters,20000,16,26,15000,5000,orf-rnd,allround,3.4,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,satellite,6435,36,6,4435,2000,orf-rnd,allround,9.1,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,zip,9298,256,10,7291,2007,orf-rnd,allround,6.2,NA,OOB,100,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,waveform,3300,21,3,300,3000,orf-rnd,allround,16,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,twonorm,3300,20,2,300,3000,orf-rnd,allround,3.8,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,threenorm,3300,20,2,300,3000,orf-rnd,allround,16.8,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ringnorm,3300,20,2,300,3000,orf-rnd,allround,4.8,NA,OOB,50,mtryTuned,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,glass,214,9,6,192,22,orf-rnd,allround,23.5,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,breast cancer,699,9,2,629,70,orf-rnd,allround,2.9,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,pima,768,8,2,691,77,orf-rnd,allround,23.1,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,sonar,208,60,2,187,21,orf-rnd,allround,13.8,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vowel,990,10,11,891,99,orf-rnd,allround,3.3,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ionosphere,351,34,2,315,36,orf-rnd,allround,5.7,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,vehicle,846,18,4,761,85,orf-rnd,allround,22.8,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,german credit,1000,24,2,900,100,orf-rnd,allround,23.8,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,image,2310,19,7,2079,231,orf-rnd,allround,1.8,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ecoli,336,7,8,302,34,orf-rnd,allround,12.4,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,votes,435,16,2,391,44,orf-rnd,allround,4,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,liver,345,6,2,310,35,orf-rnd,allround,27.2,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,letters,20000,16,26,15000,5000,orf-rnd,allround,4.1,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,satellite,6435,36,6,4435,2000,orf-rnd,allround,10.2,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,zip,9298,256,10,7291,2007,orf-rnd,allround,7.2,NA,none,100,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,waveform,3300,21,3,300,3000,orf-rnd,allround,16.1,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,twonorm,3300,20,2,300,3000,orf-rnd,allround,3.9,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,threenorm,3300,20,2,300,3000,orf-rnd,allround,16.9,NA,none,50,2,50 runs train/test,simple diff
Random forests,Breiman,2001,Machine Learning,ringnorm,3300,20,2,300,3000,orf-rnd,allround,4.6,NA,none,50,2,50 runs train/test,simple diff
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,tree,allround,29.17,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,tree,allround,21.53,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,tree,allround,16.31,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,tree,allround,26.8,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,tree,allround,20.63,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,tree,allround,3.24,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,tree,allround,8.33,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,tree,allround,14.63,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,tree,allround,3.91,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,tree,allround,15.03,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,tree,allround,14.84,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,tree,allround,26.45,NA,none,1,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,bagging,allround,19.41,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,bagging,allround,6.97,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,bagging,allround,8.12,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,bagging,allround,25.15,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,bagging,allround,7.35,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,bagging,allround,2.2,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,bagging,allround,5.73,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,bagging,allround,9.56,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,bagging,allround,1.74,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,bagging,allround,8.24,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,bagging,allround,7.44,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,bagging,allround,12.4,NA,none,100,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,rs,allround,17.45,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,rs,allround,3.76,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,rs,allround,4.57,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,rs,allround,24.16,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,rs,allround,2.73,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,rs,allround,1.72,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,rs,allround,4.1,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,rs,allround,8.14,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,rs,allround,0.9,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,rs,allround,4.87,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,rs,allround,4.39,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,rs,allround,7.85,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,rf,allround,16.97,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,rf,allround,3.54,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,rf,allround,3.64,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,rf,allround,24.73,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,rf,allround,2.73,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,rf,allround,1.72,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,rf,allround,4.1,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,rf,allround,8.14,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,rf,allround,1.02,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,rf,allround,5.23,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,rf,allround,4.87,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,rf,allround,8.43,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,erf,allround,16.6,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,erf,allround,3.15,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,erf,allround,3.27,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,erf,allround,24.02,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,erf,allround,1.47,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,erf,allround,1.4,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,erf,allround,4.15,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,erf,allround,7.96,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,erf,allround,0.6,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,erf,allround,4.48,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,erf,allround,3.75,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,erf,allround,7.08,NA,optimal-test,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,erf,allround,16.84,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,erf,allround,3.52,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,erf,allround,3.55,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,erf,allround,24.47,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,erf,allround,1.47,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,erf,allround,1.34,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,erf,allround,4.41,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,erf,allround,8.12,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,erf,allround,0.63,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,erf,allround,4.47,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,erf,allround,3.77,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,erf,allround,7.19,NA,cv-10,100,mtryTuned,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,erf,allround,16.61,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,erf,allround,3.53,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,erf,allround,3.27,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,erf,allround,26,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,erf,allround,1.74,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,erf,allround,1.77,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,erf,allround,4.17,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,erf,allround,8.43,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,erf,allround,0.67,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,erf,allround,4.49,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,erf,allround,3.8,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,erf,allround,7.61,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,erf-boot,allround,16.59,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,erf-boot,allround,3.47,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,erf-boot,allround,3.54,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,erf-boot,allround,25.72,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,erf-boot,allround,2.28,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,erf-boot,allround,2,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,erf-boot,allround,4.36,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,erf-boot,allround,8.97,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,erf-boot,allround,0.77,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,erf-boot,allround,4.82,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,erf-boot,allround,4.24,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,erf-boot,allround,8.52,NA,default,100,sqrt(p),10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,erf,allround,18.02,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,erf,allround,3.15,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,erf,allround,5.39,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,erf,allround,27.22,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,erf,allround,2.22,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,erf,allround,2.57,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,erf,allround,4.64,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,erf,allround,9.31,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,erf,allround,0.96,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,erf,allround,5.4,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,erf,allround,5.46,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,erf,allround,19.68,NA,none,100,1,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,erf,allround,17.77,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,erf,allround,5.14,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,erf,allround,5.53,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,erf,allround,24.09,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,erf,allround,2.14,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,erf,allround,1.48,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,erf,allround,4.38,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,erf,allround,8.18,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,erf,allround,0.61,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,erf,allround,4.73,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,erf,allround,4.23,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,erf,allround,8.28,NA,none,100,all,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,waveform,3300,21,3,300,4700,knn,allround,18.35,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,twonorm,3300,20,2,300,9700,knn,allround,2.78,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,ringnorm,3300,20,2,300,9700,knn,allround,36.22,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vehicle,846,18,4,761,85,knn,allround,28.09,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,vowel,990,10,11,891,99,knn,allround,1.21,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,segment,2310,19,7,2079,231,knn,allround,3.54,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,spam,4601,57,2,3221,1380,knn,allround,9.48,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,satellite,6435,36,6,4435,2000,knn,allround,9.37,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,pendigits,10992,16,10,7494,3498,knn,allround,0.63,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,dig44,18000,16,10,9000,9000,knn,allround,4.42,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,letters,20000,16,26,10000,10000,knn,allround,6.33,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Extremely randomised trees,Geurts et al.,2006,Machine Learning,isolet,7797,617,26,6238,1559,knn,allround,15.45,NA,optimal-test,NA,NA,10 runs train/test,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),german credit,1000,20,2,NA,NA,rf,allround,24.8,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),waveform,5000,21,3,NA,NA,rf,allround,15.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),horse,368,22,2,NA,NA,rf,allround,14.2,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),parkinsons,195,22,2,NA,NA,rf,allround,10.8,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),auto,205,25,6,NA,NA,rf,allround,24.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hypo,3163,25,2,NA,NA,rf,allround,1.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sick,2800,29,2,NA,NA,rf,allround,2.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ionosphere,351,34,2,NA,NA,rf,allround,6.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),anneal,898,38,5,NA,NA,rf,allround,5.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ada,4147,48,2,NA,NA,rf,allround,16,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sonar,208,60,2,NA,NA,rf,allround,19.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hillvalley,606,100,2,NA,NA,rf,allround,45.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),musk,476,166,2,NA,NA,rf,allround,13.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arrhythmia,452,279,13,NA,NA,rf,allround,31.8,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),madelon,2000,500,2,NA,NA,rf,allround,32.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),gina,3153,970,2,NA,NA,rf,allround,7.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hiva,3845,1617,2,NA,NA,rf,allround,3.3,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arcene,100,10000,2,NA,NA,rf,allround,24,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),german credit,1000,20,2,NA,NA,rrf,select,25,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),waveform,5000,21,3,NA,NA,rrf,select,15.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),horse,368,22,2,NA,NA,rrf,select,15.3,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),parkinsons,195,22,2,NA,NA,rrf,select,10.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),auto,205,25,6,NA,NA,rrf,select,24.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hypo,3163,25,2,NA,NA,rrf,select,1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sick,2800,29,2,NA,NA,rrf,select,1.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ionosphere,351,34,2,NA,NA,rrf,select,7.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),anneal,898,38,5,NA,NA,rrf,select,6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ada,4147,48,2,NA,NA,rrf,select,16.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sonar,208,60,2,NA,NA,rrf,select,21.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hillvalley,606,100,2,NA,NA,rrf,select,48.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),musk,476,166,2,NA,NA,rrf,select,85.3,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arrhythmia,452,279,13,NA,NA,rrf,select,30.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),madelon,2000,500,2,NA,NA,rrf,select,29.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),gina,3153,970,2,NA,NA,rrf,select,8.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hiva,3845,1617,2,NA,NA,rrf,select,3.3,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arcene,100,10000,2,NA,NA,rrf,select,31.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),german credit,1000,20,2,NA,NA,rboost-select,select,25,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),waveform,5000,21,3,NA,NA,rboost-select,select,15.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),horse,368,22,2,NA,NA,rboost-select,select,14.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),parkinsons,195,22,2,NA,NA,rboost-select,select,10.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),auto,205,25,6,NA,NA,rboost-select,select,24.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hypo,3163,25,2,NA,NA,rboost-select,select,1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sick,2800,29,2,NA,NA,rboost-select,select,2,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ionosphere,351,34,2,NA,NA,rboost-select,select,7.2,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),anneal,898,38,5,NA,NA,rboost-select,select,5.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ada,4147,48,2,NA,NA,rboost-select,select,16.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sonar,208,60,2,NA,NA,rboost-select,select,22.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hillvalley,606,100,2,NA,NA,rboost-select,select,48.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),musk,476,166,2,NA,NA,rboost-select,select,14.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arrhythmia,452,279,13,NA,NA,rboost-select,select,30.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),madelon,2000,500,2,NA,NA,rboost-select,select,32.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),gina,3153,970,2,NA,NA,rboost-select,select,8.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hiva,3845,1617,2,NA,NA,rboost-select,select,2.3,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arcene,100,10000,2,NA,NA,rboost-select,select,32.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),german credit,1000,20,2,NA,NA,CFS-select,select,29.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),waveform,5000,21,3,NA,NA,CFS-select,select,15.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),horse,368,22,2,NA,NA,CFS-select,select,17.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),parkinsons,195,22,2,NA,NA,CFS-select,select,12.2,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),auto,205,25,6,NA,NA,CFS-select,select,25.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hypo,3163,25,2,NA,NA,CFS-select,select,1.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sick,2800,29,2,NA,NA,CFS-select,select,3.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ionosphere,351,34,2,NA,NA,CFS-select,select,7.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),anneal,898,38,5,NA,NA,CFS-select,select,9.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ada,4147,48,2,NA,NA,CFS-select,select,17.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sonar,208,60,2,NA,NA,CFS-select,select,26.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hillvalley,606,100,2,NA,NA,CFS-select,select,51.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),musk,476,166,2,NA,NA,CFS-select,select,16,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arrhythmia,452,279,13,NA,NA,CFS-select,select,27.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),madelon,2000,500,2,NA,NA,CFS-select,select,21.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),gina,3153,970,2,NA,NA,CFS-select,select,10.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hiva,3845,1617,2,NA,NA,CFS-select,select,3.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arcene,100,10000,2,NA,NA,CFS-select,select,28.7,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),german credit,1000,20,2,NA,NA,FCBF-select,select,31.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),waveform,5000,21,3,NA,NA,FCBF-select,select,21.2,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),horse,368,22,2,NA,NA,FCBF-select,select,17.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),parkinsons,195,22,2,NA,NA,FCBF-select,select,15.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),auto,205,25,6,NA,NA,FCBF-select,select,28.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hypo,3163,25,2,NA,NA,FCBF-select,select,1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sick,2800,29,2,NA,NA,FCBF-select,select,3.4,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ionosphere,351,34,2,NA,NA,FCBF-select,select,8.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),anneal,898,38,5,NA,NA,FCBF-select,select,8.1,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),ada,4147,48,2,NA,NA,FCBF-select,select,16.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),sonar,208,60,2,NA,NA,FCBF-select,select,26.6,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hillvalley,606,100,2,NA,NA,FCBF-select,select,50.2,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),musk,476,166,2,NA,NA,FCBF-select,select,17.9,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arrhythmia,452,279,13,NA,NA,FCBF-select,select,31.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),madelon,2000,500,2,NA,NA,FCBF-select,select,39.8,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),gina,3153,970,2,NA,NA,FCBF-select,select,16.8,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),hiva,3845,1617,2,NA,NA,FCBF-select,select,3.5,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Feature selection via regularised trees,Deng and Runger,2012,Neural Networks (IJCNN),arcene,100,10000,2,NA,NA,FCBF-select,select,29.8,NA,default,200,sqrt(p),10 runs 2-cv,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,rrf(1)-rf,select-HD,15.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,rrf(1)-rf,select-HD,15.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,rrf(1)-rf,select-HD,35.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,rrf(1)-rf,select-HD,39.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,rrf(1)-rf,select-HD,16.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,rrf(1)-rf,select-HD,5.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,rrf(1)-rf,select-HD,1.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,rrf(1)-rf,select-HD,33.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,rrf(1)-rf,select-HD,8.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,rrf(1)-rf,select-HD,2.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,rf,select-HD,15.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,rf,select-HD,17,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,rf,select-HD,37.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,rf,select-HD,41.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,rf,select-HD,15.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,rf,select-HD,6.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,rf,select-HD,0.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,rf,select-HD,32.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,rf,select-HD,10.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,rf,select-HD,3.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,grrf(0.1)-rf,select-HD,16.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,grrf(0.1)-rf,select-HD,21.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,grrf(0.1)-rf,select-HD,34.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,grrf(0.1)-rf,select-HD,38.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,grrf(0.1)-rf,select-HD,17.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,grrf(0.1)-rf,select-HD,8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,grrf(0.1)-rf,select-HD,6.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,grrf(0.1)-rf,select-HD,38.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,grrf(0.1)-rf,select-HD,8.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,grrf(0.1)-rf,select-HD,6.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,grrf(0.2)-rf,select-HD,16.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,grrf(0.2)-rf,select-HD,25.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,grrf(0.2)-rf,select-HD,35.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,grrf(0.2)-rf,select-HD,40.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,grrf(0.2)-rf,select-HD,18.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,grrf(0.2)-rf,select-HD,9.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,grrf(0.2)-rf,select-HD,7.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,grrf(0.2)-rf,select-HD,45.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,grrf(0.2)-rf,select-HD,8.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,grrf(0.2)-rf,select-HD,7.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,rrf(0.9)-rf,select-HD,16,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,rrf(0.9)-rf,select-HD,23.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,rrf(0.9)-rf,select-HD,36.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,rrf(0.9)-rf,select-HD,41,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,rrf(0.9)-rf,select-HD,19,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,rrf(0.9)-rf,select-HD,9.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,rrf(0.9)-rf,select-HD,9.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,rrf(0.9)-rf,select-HD,40.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,rrf(0.9)-rf,select-HD,10.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,rrf(0.9)-rf,select-HD,7.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,varSelRF,select-HD,21.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,varSelRF,select-HD,23.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,varSelRF,select-HD,38.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,varSelRF,select-HD,41.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,varSelRF,select-HD,23.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,varSelRF,select-HD,10.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,varSelRF,select-HD,2.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,varSelRF,select-HD,41.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,varSelRF,select-HD,8.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,varSelRF,select-HD,3.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,adenocarcinoma,76,9868,2,50,26,lasso-rf,select-HD,18.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,brain,42,5597,5,27,15,lasso-rf,select-HD,25.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.2.class,77,4869,2,50,27,lasso-rf,select-HD,36.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,breast.3.class,95,4869,3,62,33,lasso-rf,select-HD,40,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,colon,62,2000,2,40,22,lasso-rf,select-HD,18,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,leukemia,38,3051,2,25,13,lasso-rf,select-HD,7.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,lymphoma,62,4026,3,40,22,lasso-rf,select-HD,0.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,nci60,61,5244,8,40,21,lasso-rf,select-HD,39.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,prostate,102,6033,2,67,35,lasso-rf,select-HD,8.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Gene selection with guided regularised random forest,Deng and Runger,2013,Pattern Recognition,srbct,63,2308,4,41,22,lasso-rf,select-HD,0.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,rotationForest-prune,allround,1.07,0.95,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,rotationForest-prune,allround,20.2,6.92,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,rotationForest-prune,allround,17.5,8.66,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,rotationForest-prune,allround,9.67,2.52,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,rotationForest-prune,allround,27.34,6.71,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,rotationForest-prune,allround,17.15,6.26,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,rotationForest-prune,allround,13.87,3.88,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,rotationForest-prune,allround,25.9,3.93,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,rotationForest-prune,allround,25.73,8.11,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,rotationForest-prune,allround,17.75,6.43,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,rotationForest-prune,allround,17.2,8.91,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,rotationForest-prune,allround,15.27,5.44,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,rotationForest-prune,allround,19.72,6.33,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,rotationForest-prune,allround,0.44,0.35,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,rotationForest-prune,allround,6.12,3.68,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,rotationForest-prune,allround,4.27,5.2,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,rotationForest-prune,allround,8.44,11.91,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,rotationForest-prune,allround,4.52,0.47,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,rotationForest-prune,allround,16.01,8.33,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,rotationForest-prune,allround,0.8,0.26,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,rotationForest-prune,allround,23.52,4.44,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,rotationForest-prune,allround,54.94,6.4,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,rotationForest-prune,allround,1.95,0.95,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,rotationForest-prune,allround,16.44,7.84,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,rotationForest-prune,allround,5.23,2.36,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,rotationForest-prune,allround,4.53,1.15,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,rotationForest-prune,allround,21.95,3.64,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,rotationForest-prune,allround,3.74,2.79,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,rotationForest-prune,allround,4.32,1.95,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,rotationForest-prune,allround,16.07,1.69,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,rotationForest-prune,allround,2.96,1.94,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,rotationForest-prune,allround,7.85,8.22,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,tree-prune,allround,1.39,1.06,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,tree-prune,allround,22.76,7.04,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,tree-prune,allround,17.66,9.22,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,tree-prune,allround,22.18,3.69,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,tree-prune,allround,25.81,6.05,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,tree-prune,allround,23.29,6.84,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,tree-prune,allround,14.37,4.12,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,tree-prune,allround,28.91,3.53,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,tree-prune,allround,32.45,9.33,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,tree-prune,allround,21.78,7.2,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,tree-prune,allround,20.42,9.28,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,tree-prune,allround,14.84,5.7,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,tree-prune,allround,19.92,7.65,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,tree-prune,allround,0.47,0.35,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,tree-prune,allround,10.09,4.57,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,tree-prune,allround,5.11,5.03,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,tree-prune,allround,20.44,15.78,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,tree-prune,allround,11.96,0.73,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,tree-prune,allround,23.63,11.09,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,tree-prune,allround,3.54,0.56,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,tree-prune,allround,25.62,4.91,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,tree-prune,allround,58.29,6.83,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,tree-prune,allround,3.21,1.28,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,tree-prune,allround,26.02,8.67,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,tree-prune,allround,8.1,3.11,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,tree-prune,allround,5.83,1.22,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,tree-prune,allround,27.67,4.42,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,tree-prune,allround,3.51,2.65,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,tree-prune,allround,20.84,4.58,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,tree-prune,allround,24.73,2,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,tree-prune,allround,5.13,2.69,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,tree-prune,allround,7.44,7.04,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,bagging-prune,allround,1.11,0.92,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,bagging-prune,allround,18.97,7.36,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,bagging-prune,allround,17.31,8.6,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,bagging-prune,allround,18.15,3.74,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,bagging-prune,allround,27.35,6.12,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,bagging-prune,allround,20.79,6.74,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,bagging-prune,allround,14.22,4.02,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,bagging-prune,allround,26.25,3.62,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,bagging-prune,allround,26.03,9.41,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,bagging-prune,allround,19.26,6.66,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,bagging-prune,allround,18.76,8.22,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,bagging-prune,allround,14.59,5.7,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,bagging-prune,allround,20.38,6.7,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,bagging-prune,allround,0.42,0.32,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,bagging-prune,allround,7.75,3.8,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,bagging-prune,allround,5.33,5.12,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,bagging-prune,allround,16.87,15.2,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,bagging-prune,allround,7.28,0.63,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,bagging-prune,allround,22.03,10.22,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,bagging-prune,allround,2.07,0.47,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,bagging-prune,allround,24.35,4.45,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,bagging-prune,allround,56.26,6.76,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,bagging-prune,allround,2.51,1.07,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,bagging-prune,allround,21.69,9.11,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,bagging-prune,allround,7.27,2.87,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,bagging-prune,allround,5.57,1.26,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,bagging-prune,allround,25.55,4.18,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,bagging-prune,allround,3.63,2.54,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,bagging-prune,allround,10.55,3.22,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,bagging-prune,allround,18.25,1.7,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,bagging-prune,allround,4.01,2.44,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,bagging-prune,allround,6.7,7.07,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,boosting-prune,allround,0.42,0.71,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,boosting-prune,allround,15.1,7.07,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,boosting-prune,allround,14.69,6.99,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,boosting-prune,allround,21.54,4.07,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,boosting-prune,allround,33.12,7.37,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,boosting-prune,allround,20.62,6.99,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,boosting-prune,allround,16.14,4.35,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,boosting-prune,allround,28.99,3.93,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,boosting-prune,allround,24.8,8.26,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,boosting-prune,allround,21.73,7.2,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,boosting-prune,allround,17.54,8,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,boosting-prune,allround,18.37,6.11,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,boosting-prune,allround,21.25,6.65,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,boosting-prune,allround,0.36,0.3,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,boosting-prune,allround,6.82,4.02,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,boosting-prune,allround,5.73,5.18,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,boosting-prune,allround,12.69,13.36,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,boosting-prune,allround,4.47,0.47,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,boosting-prune,allround,18.27,8.61,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,boosting-prune,allround,0.98,0.3,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,boosting-prune,allround,28.04,4.53,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,boosting-prune,allround,58.13,6.53,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,boosting-prune,allround,1.86,0.89,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,boosting-prune,allround,20.21,8.63,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,boosting-prune,allround,7.26,2.82,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,boosting-prune,allround,5.4,1.15,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,boosting-prune,allround,24.22,4.19,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,boosting-prune,allround,4.66,3.11,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,boosting-prune,allround,7.87,2.84,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,boosting-prune,allround,18.66,1.88,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,boosting-prune,allround,3.94,2.27,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,boosting-prune,allround,3.62,5.75,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,rotationForest,allround,0.99,0.93,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,rotationForest,allround,20.17,6.93,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,rotationForest,allround,17.44,8.66,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,rotationForest,allround,9.74,2.62,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,rotationForest,allround,27.93,6.54,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,rotationForest,allround,17.39,6.12,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,rotationForest,allround,14,3.9,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,rotationForest,allround,25.67,8.06,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,rotationForest,allround,26.13,3.89,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,rotationForest,allround,17.63,6.45,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,rotationForest,allround,17.08,8.88,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,rotationForest,allround,15.2,5.35,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,rotationForest,allround,20.43,6.45,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,rotationForest,allround,0.43,0.33,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,rotationForest,allround,6.12,3.76,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,rotationForest,allround,4.27,5.2,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,rotationForest,allround,8.31,11.89,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,rotationForest,allround,4.46,0.47,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,rotationForest,allround,15.73,8.35,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,rotationForest,allround,0.79,0.25,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,rotationForest,allround,23.61,4.43,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,rotationForest,allround,55.63,6.56,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,rotationForest,allround,1.95,0.95,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,rotationForest,allround,16.51,7.88,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,rotationForest,allround,5.83,2.47,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,rotationForest,allround,4.51,1.13,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,rotationForest,allround,22.05,3.74,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,rotationForest,allround,3.13,2.88,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,rotationForest,allround,4.23,1.94,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,rotationForest,allround,16.06,1.72,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,rotationForest,allround,2.98,1.93,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,rotationForest,allround,7.65,8.04,fixed,10,m=3,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,tree,allround,1.38,1.01,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,tree,allround,23.67,7.45,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,tree,allround,17.14,9.25,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,tree,allround,20.57,4.01,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,tree,allround,32,7.43,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,tree,allround,23.51,6.91,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,tree,allround,17.5,4.24,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,tree,allround,32.23,9.7,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,tree,allround,32.11,3.95,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,tree,allround,23.31,7.51,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,tree,allround,21.05,9.27,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,tree,allround,17.84,5.89,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,tree,allround,21.15,7.3,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,tree,allround,0.49,0.37,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,tree,allround,10.03,4.55,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,tree,allround,5.07,4.99,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,tree,allround,20.16,14.57,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,tree,allround,11.98,0.75,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,tree,allround,24.36,11.12,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,tree,allround,3.54,0.57,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,tree,allround,26.15,4.94,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,tree,allround,57.58,7.57,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,tree,allround,3.19,1.26,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,tree,allround,26.18,8.71,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,tree,allround,9.33,3.34,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,tree,allround,7.8,1.37,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,tree,allround,27.62,4.25,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,tree,allround,4.29,2.93,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,tree,allround,20.78,4.59,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,tree,allround,24.86,1.99,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,tree,allround,5.7,2.74,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,tree,allround,6.58,6.93,none,1,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,bagging,allround,1.02,0.93,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,bagging,allround,18.88,7.35,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,bagging,allround,15.88,8.42,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,bagging,allround,18.61,3.7,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,bagging,allround,30.52,7.17,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,bagging,allround,20.3,6.01,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,bagging,allround,14.83,4.34,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,bagging,allround,26.15,9.34,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,bagging,allround,27.92,3.63,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,bagging,allround,19.56,6.84,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,bagging,allround,19.32,8.89,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,bagging,allround,15.2,5.96,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,bagging,allround,21.26,6.65,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,bagging,allround,0.41,0.3,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,bagging,allround,7.71,3.79,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,bagging,allround,5.42,5.15,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,bagging,allround,15.69,14.44,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,bagging,allround,7.15,0.65,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,bagging,allround,21.03,10.31,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,bagging,allround,2.01,0.44,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,bagging,allround,24.41,4.54,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,bagging,allround,57.21,6.92,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,bagging,allround,2.42,1.05,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,bagging,allround,21.66,9.14,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,bagging,allround,8.12,3.15,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,bagging,allround,5.75,1.2,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,bagging,allround,25.3,4.07,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,bagging,allround,3.57,2.47,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,bagging,allround,10.48,3.27,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,bagging,allround,18.22,1.74,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,bagging,allround,4.18,2.54,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,bagging,allround,6.5,7.11,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,boosting,allround,0.46,0.68,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,boosting,allround,16.7,6.99,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,boosting,allround,15.39,7.93,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,boosting,allround,23.18,4.14,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,boosting,allround,33.88,7.81,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,boosting,allround,20.8,7.25,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,boosting,allround,15.98,3.98,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,boosting,allround,23.77,9.09,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,boosting,allround,28.05,4.32,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,boosting,allround,20.62,7.4,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,boosting,allround,17.55,8.17,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,boosting,allround,18.95,6.2,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,boosting,allround,20.92,7,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,boosting,allround,0.35,0.3,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,boosting,allround,6.99,3.97,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,boosting,allround,5.64,5.22,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,boosting,allround,12.8,13.81,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,boosting,allround,4.56,0.5,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,boosting,allround,17.6,9.73,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,boosting,allround,0.99,0.28,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,boosting,allround,27.51,5.08,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,boosting,allround,58.36,6.94,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,boosting,allround,1.75,0.8,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,boosting,allround,20.05,9.51,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,boosting,allround,7.56,2.76,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,boosting,allround,5.89,1.23,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,boosting,allround,23.56,4.01,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,boosting,allround,4.78,3.19,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,boosting,allround,8.07,2.72,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,boosting,allround,18.55,1.71,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,boosting,allround,4.03,2.11,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,boosting,allround,2.96,5.21,none,10,NA,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,anneal,898,38,6,NA,NA,rf,allround,0.62,0.78,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,audiology,226,69,24,NA,NA,rf,allround,23.42,7.94,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,auto,205,26,7,NA,NA,rf,allround,18.05,7.85,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,balance,625,4,3,NA,NA,rf,allround,19.72,3.8,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer alt,286,10,2,NA,NA,rf,allround,31,7.31,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,cleveland heart,307,13,5,NA,NA,rf,allround,19.66,6.47,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,credit,690,15,2,NA,NA,rf,allround,14.85,4.23,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,german credit,1000,20,2,NA,NA,rf,allround,24.35,8.42,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,glass,214,9,7,NA,NA,rf,allround,26.43,3.38,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,heart,270,13,2,NA,NA,rf,allround,19.14,6.53,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hepatitis,155,19,2,NA,NA,rf,allround,16.96,8.07,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,horse,368,23,2,NA,NA,rf,allround,15.04,5.43,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hung heart,294,13,5,NA,NA,rf,allround,20.72,6.31,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,hypothyroid,3772,29,4,NA,NA,rf,allround,0.82,0.46,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,ionosphere,351,34,2,NA,NA,rf,allround,7.16,3.89,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,iris,150,4,3,NA,NA,rf,allround,5.87,5.18,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,labor,57,16,2,NA,NA,rf,allround,13,13.45,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,letters,20000,16,26,NA,NA,rf,allround,5.48,0.49,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,lymphoma,148,18,4,NA,NA,rf,allround,18.72,8.58,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pendigits,10992,16,10,NA,NA,rf,allround,1.19,0.29,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,pima,768,8,2,NA,NA,rf,allround,25.22,4.42,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,tumor,239,17,22,NA,NA,rf,allround,58.44,6.5,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,segment,2310,19,7,NA,NA,rf,allround,2.29,1.06,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,sonar,208,60,2,NA,NA,rf,allround,19.25,7.84,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,soybean,683,35,19,NA,NA,rf,allround,8.08,2.83,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,splice,3190,60,3,NA,NA,rf,allround,9.93,1.79,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vehicle,846,18,4,NA,NA,rf,allround,25.63,4.43,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,votes,435,16,2,NA,NA,rf,allround,4.26,2.75,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,vowel,990,10,11,NA,NA,rf,allround,7.63,2.73,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,waveform,5000,40,3,NA,NA,rf,allround,18.11,1.74,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,breast cancer,699,9,2,NA,NA,rf,allround,4.25,2.14,default,10,log(p) + 1,15 runs 10-cv,corrected SD
Rotation forest,Rodriguez et al.,2006,Pattern Analysis and Machine Intelligence,zoo,101,18,7,NA,NA,rf,allround,4.17,6.02,default,10,log(p) + 1,15 runs 10-cv,corrected SD
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,breast cancer,699,9,2,461,238,rf,allround,30.82,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,audit,NA,NA,NA,NA,NA,rf,allround,3.47,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,credit,690,15,2,455,235,rf,allround,22.53,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,pasture,NA,NA,NA,NA,NA,rf,allround,58.33,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,squash-unstored,52,24,3,34,18,rf,allround,27.5,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,squash-stored,52,24,3,34,18,rf,allround,44.17,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,white clover,NA,NA,NA,NA,NA,rf,allround,22.86,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,eucalyptus,NA,NA,NA,NA,NA,rf,allround,51.6,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,soybean,683,35,19,450,233,rf,allround,17.68,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,pima,768,8,2,506,262,rf,allround,18.74,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,glass,214,9,7,141,73,rf,allround,32.47,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,car,1728,6,4,1140,588,rf,allround,37.74,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,sonar,208,60,2,137,71,rf,allround,99.86,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,vehicle,846,18,4,558,288,rf,allround,30.1,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,votes,435,16,2,287,148,rf,allround,4.05,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,breast cancer,699,9,2,461,238,club-drf,online,27.42,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,audit,NA,NA,NA,NA,NA,club-drf,online,3.53,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,credit,690,15,2,455,235,club-drf,online,20.32,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,pasture,NA,NA,NA,NA,NA,club-drf,online,44.17,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,squash-unstored,52,24,3,34,18,club-drf,online,20,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,squash-stored,52,24,3,34,18,club-drf,online,35,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,white clover,NA,NA,NA,NA,NA,club-drf,online,20.71,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,eucalyptus,NA,NA,NA,NA,NA,club-drf,online,43.8,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,soybean,683,35,19,450,233,club-drf,online,15.89,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,pima,768,8,2,506,262,club-drf,online,18.58,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,glass,214,9,7,141,73,club-drf,online,23.84,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,car,1728,6,4,1140,588,club-drf,online,35.83,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,sonar,208,60,2,137,71,club-drf,online,87.75,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,vehicle,846,18,4,558,288,club-drf,online,27.57,NA,default,500,sqrt(p),10 runs train/test,simple diff
On extreme pruning of random forest ensembles,Fawagreh et al.,2014,arXiv preprint,votes,435,16,2,287,148,club-drf,online,4.19,NA,default,500,sqrt(p),10 runs train/test,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,colon,62,2000,2,31,31,varSelRF,allround-interp,0.2,NA,fixed,2000,100,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,leukemia,38,3051,2,19,19,varSelRF,allround-interp,0,NA,fixed,2000,100,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,lymphoma,62,4026,3,31,31,varSelRF,allround-interp,0.09,NA,fixed,2000,100,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,prostate,102,6033,2,51,51,varSelRF,allround-interp,0.075,NA,fixed,2000,100,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,colon,62,2000,2,31,31,rf,allround,0.14,NA,default,2000,p/3,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,leukemia,38,3051,2,19,19,rf,allround,0.02,NA,default,2000,p/3,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,lymphoma,62,4026,3,31,31,rf,allround,0.1,NA,default,2000,p/3,5-fold cv,simple diff
Variable selection using random forests,Genuer et al.,2012,Pattern Recognition Letters,prostate,102,6033,2,51,51,rf,allround,0.07,NA,default,2000,p/3,5-fold cv,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),mnist,58000,780,2,48000,10000,rf,HD,3.88,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),tis,12075,927,2,5200,6875,rf,HD,16.21,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),fbis,2463,2000,17,1711,752,rf,HD,22.21,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),re1,1657,3758,25,1147,510,rf,HD,39.21,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),gisette,6000,5000,2,5000,1000,rf,HD,4.47,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),newsgroup,18772,5000,20,11268,7504,rf,HD,38.62,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),wap,1560,8460,20,1104,456,rf,HD,31.79,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),la2s,2700,12432,6,1855,845,rf,HD,67.45,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),la1s,2850,13195,6,1963,887,rf,HD,67.53,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),mnist,58000,780,2,48000,10000,rf-cs,HD,2.66,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),tis,12075,927,2,5200,6875,rf-cs,HD,9.28,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),fbis,2463,2000,17,1711,752,rf-cs,HD,16.76,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),re1,1657,3758,25,1147,510,rf-cs,HD,15.49,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),gisette,6000,5000,2,5000,1000,rf-cs,HD,3.1,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),newsgroup,18772,5000,20,11268,7504,rf-cs,HD,29.62,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),wap,1560,8460,20,1104,456,rf-cs,HD,19.96,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),la2s,2700,12432,6,1855,845,rf-cs,HD,11.43,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),la1s,2850,13195,6,1963,887,rf-cs,HD,13.53,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),mnist,58000,780,2,48000,10000,rf-igr,HD,2.63,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),tis,12075,927,2,5200,6875,rf-igr,HD,9.25,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),fbis,2463,2000,17,1711,752,rf-igr,HD,17.69,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),re1,1657,3758,25,1147,510,rf-igr,HD,16.67,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),gisette,6000,5000,2,5000,1000,rf-igr,HD,3.7,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),newsgroup,18772,5000,20,11268,7504,rf-igr,HD,27.67,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),wap,1560,8460,20,1104,456,rf-igr,HD,18.21,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),la2s,2700,12432,6,1855,845,rf-igr,HD,12.78,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Classifying very high-dimensional data with random forests built from small subspaces,Xu et al.,2012,International Journal of Data Warehousing and Mining (IJDWM),la1s,2850,13195,6,1963,887,rf-igr,HD,13.3,NA,default,100,log(p) + 1,80 runs train/test,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,ringnorm,7400,20,2,4884,2516,tree-c4.5,allround,10.7,NA,none,NA,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,satellite,6435,36,6,4247,2188,tree-c4.5,allround,16,NA,none,NA,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,image,2310,18,7,1524,786,tree-c4.5,allround,6.4,NA,none,NA,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,dna,3186,60,3,2102,1084,tree-c4.5,allround,13.5,NA,none,NA,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,letters,20000,16,26,13200,6800,tree-c4.5,allround,18.6,NA,none,NA,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,ringnorm,7400,20,2,4884,2516,bagging,allround,6,NA,none,ntree-tuning,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,satellite,6435,36,6,4247,2188,bagging,allround,11.8,NA,none,ntree-tuning,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,image,2310,18,7,1524,786,bagging,allround,3.9,NA,none,ntree-tuning,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,dna,3186,60,3,2102,1084,bagging,allround,10.5,NA,none,ntree-tuning,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,letters,20000,16,26,13200,6800,bagging,allround,11.4,NA,none,ntree-tuning,NA,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,ringnorm,7400,20,2,4884,2516,rs,allround,2.3,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,satellite,6435,36,6,4247,2188,rs,allround,10.2,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,image,2310,18,7,1524,786,rs,allround,3.8,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,dna,3186,60,3,2102,1084,rs,allround,10,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,letters,20000,16,26,13200,6800,rs,allround,9.6,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,ringnorm,7400,20,2,4884,2516,bag-rs,allround,1.6,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,satellite,6435,36,6,4247,2188,bag-rs,allround,10.4,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,image,2310,18,7,1524,786,bag-rs,allround,4,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,dna,3186,60,3,2102,1084,bag-rs,allround,8.5,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,letters,20000,16,26,13200,6800,bag-rs,allround,8.4,NA,cv-10,ntree-tuning,mtryTuned,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,ringnorm,7400,20,2,4884,2516,rf,allround,3.8,NA,default,ntree-tuning,log(p) + 1,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,satellite,6435,36,6,4247,2188,rf,allround,10.9,NA,default,ntree-tuning,log(p) + 1,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,image,2310,18,7,1524,786,rf,allround,5.5,NA,default,ntree-tuning,log(p) + 1,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,dna,3186,60,3,2102,1084,rf,allround,10.2,NA,default,ntree-tuning,log(p) + 1,3-fold cv,simple diff
Limiting the number of trees in random forests,Latinne et al.,2001,Multiple Classifier Systems,letters,20000,16,26,13200,6800,rf,allround,11,NA,default,ntree-tuning,log(p) + 1,3-fold cv,simple diff
Improving random forests,Robnik-Sikonja,2004,Machine Learning,breast cancer,699,9,2,629,70,rf,allround,3.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,bupa,345,6,2,310,35,rf,allround,26.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,pima,768,8,2,691,77,rf,allround,23.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ecoli,336,7,8,302,34,rf,allround,13.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,german credit,1000,24,2,900,100,rf,allround,25,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,glass,214,9,7,192,22,rf,allround,23.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ionosphere,351,34,2,315,36,rf,allround,6.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,letters,20000,16,26,15000,5000,rf,allround,4.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity2,200,12,2,180,20,rf,allround,18,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity3,200,13,2,180,20,rf,allround,42.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,satellite,6435,36,6,4435,2000,rf,allround,9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,segment,2310,19,7,2079,231,rf,allround,1.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,sonar,208,60,2,187,21,rf,allround,18.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vehicle,846,18,4,761,85,rf,allround,25,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,votes,435,16,2,391,44,rf,allround,4.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vowel,990,11,11,891,99,rf,allround,2.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,zip,9298,256,10,7291,2007,rf,allround,6.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,adult,5908,14,2,5317,591,rf,allround,15.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,audiology,226,69,24,203,23,rf,allround,26.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,banding,138,29,2,124,14,rf,allround,26.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,aus credit,690,15,2,621,69,rf,allround,13.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,heart,270,13,2,243,27,rf,allround,17,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,hepatitis,155,19,2,139,16,rf,allround,16.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,house,91,16,2,81,10,rf,allround,4.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,iris,150,4,3,135,15,rf,allround,4.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,lymphoma,148,18,4,133,15,rf,allround,18.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,mushroom,8124,22,2,7311,813,rf,allround,0,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,post-opt,90,8,3,81,9,rf,allround,34.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,tumor,339,17,22,305,34,rf,allround,56,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,promotor,106,57,2,95,11,rf,allround,8.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,rheumatism,355,32,6,319,36,rf,allround,30.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,spam,4601,57,2,4140,461,rf,allround,4.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,splice,3190,60,3,2871,319,rf,allround,3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,yeast,1484,8,10,1335,149,rf,allround,38.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,breast cancer,699,9,2,629,70,rf-5est,allround,3.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,bupa,345,6,2,310,35,rf-5est,allround,28.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,pima,768,8,2,691,77,rf-5est,allround,23,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ecoli,336,7,8,302,34,rf-5est,allround,12.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,german credit,1000,24,2,900,100,rf-5est,allround,25,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,glass,214,9,7,192,22,rf-5est,allround,22.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ionosphere,351,34,2,315,36,rf-5est,allround,6.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,letters,20000,16,26,15000,5000,rf-5est,allround,3.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity2,200,12,2,180,20,rf-5est,allround,13.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity3,200,13,2,180,20,rf-5est,allround,36.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,satellite,6435,36,6,4435,2000,rf-5est,allround,9.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,segment,2310,19,7,2079,231,rf-5est,allround,1.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,sonar,208,60,2,187,21,rf-5est,allround,20.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vehicle,846,18,4,761,85,rf-5est,allround,24.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,votes,435,16,2,391,44,rf-5est,allround,4.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vowel,990,11,11,891,99,rf-5est,allround,2.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,zip,9298,256,10,7291,2007,rf-5est,allround,6.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,adult,5908,14,2,5317,591,rf-5est,allround,14.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,audiology,226,69,24,203,23,rf-5est,allround,27.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,banding,138,29,2,124,14,rf-5est,allround,28.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,aus credit,690,15,2,621,69,rf-5est,allround,12.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,heart,270,13,2,243,27,rf-5est,allround,16.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,hepatitis,155,19,2,139,16,rf-5est,allround,14.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,house,91,16,2,81,10,rf-5est,allround,4.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,iris,150,4,3,135,15,rf-5est,allround,4.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,lymphoma,148,18,4,133,15,rf-5est,allround,17.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,mushroom,8124,22,2,7311,813,rf-5est,allround,0,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,post-opt,90,8,3,81,9,rf-5est,allround,30,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,tumor,339,17,22,305,34,rf-5est,allround,56.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,promotor,106,57,2,95,11,rf-5est,allround,11.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,rheumatism,355,32,6,319,36,rf-5est,allround,31,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,spam,4601,57,2,4140,461,rf-5est,allround,4.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,splice,3190,60,3,2871,319,rf-5est,allround,3.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,yeast,1484,8,10,1335,149,rf-5est,allround,38.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,breast cancer,699,9,2,629,70,rf-wv,allround,3.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,bupa,345,6,2,310,35,rf-wv,allround,26.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,pima,768,8,2,691,77,rf-wv,allround,23,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ecoli,336,7,8,302,34,rf-wv,allround,13.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,german credit,1000,24,2,900,100,rf-wv,allround,24,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,glass,214,9,7,192,22,rf-wv,allround,20.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ionosphere,351,34,2,315,36,rf-wv,allround,6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,letters,20000,16,26,15000,5000,rf-wv,allround,4.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity2,200,12,2,180,20,rf-wv,allround,12.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity3,200,13,2,180,20,rf-wv,allround,37.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,satellite,6435,36,6,4435,2000,rf-wv,allround,9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,segment,2310,19,7,2079,231,rf-wv,allround,1.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,sonar,208,60,2,187,21,rf-wv,allround,13.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vehicle,846,18,4,761,85,rf-wv,allround,24.5,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,votes,435,16,2,391,44,rf-wv,allround,4.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vowel,990,11,11,891,99,rf-wv,allround,2.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,zip,9298,256,10,7291,2007,rf-wv,allround,6.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,adult,5908,14,2,5317,591,rf-wv,allround,15.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,audiology,226,69,24,203,23,rf-wv,allround,21.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,banding,138,29,2,124,14,rf-wv,allround,20.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,aus credit,690,15,2,621,69,rf-wv,allround,13.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,heart,270,13,2,243,27,rf-wv,allround,16.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,hepatitis,155,19,2,139,16,rf-wv,allround,14.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,house,91,16,2,81,10,rf-wv,allround,4.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,iris,150,4,3,135,15,rf-wv,allround,4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,lymphoma,148,18,4,133,15,rf-wv,allround,17.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,mushroom,8124,22,2,7311,813,rf-wv,allround,0,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,post-opt,90,8,3,81,9,rf-wv,allround,32.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,tumor,339,17,22,305,34,rf-wv,allround,54,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,promotor,106,57,2,95,11,rf-wv,allround,7.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,rheumatism,355,32,6,319,36,rf-wv,allround,29.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,spam,4601,57,2,4140,461,rf-wv,allround,4.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,splice,3190,60,3,2871,319,rf-wv,allround,3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,yeast,1484,8,10,1335,149,rf-wv,allround,38.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,breast cancer,699,9,2,629,70,rf-wv-5est,allround,3.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,bupa,345,6,2,310,35,rf-wv-5est,allround,28.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,pima,768,8,2,691,77,rf-wv-5est,allround,22.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ecoli,336,7,8,302,34,rf-wv-5est,allround,13.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,german credit,1000,24,2,900,100,rf-wv-5est,allround,24.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,glass,214,9,7,192,22,rf-wv-5est,allround,21.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,ionosphere,351,34,2,315,36,rf-wv-5est,allround,6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,letters,20000,16,26,15000,5000,rf-wv-5est,allround,3.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity2,200,12,2,180,20,rf-wv-5est,allround,9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,parity3,200,13,2,180,20,rf-wv-5est,allround,26,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,satellite,6435,36,6,4435,2000,rf-wv-5est,allround,9.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,segment,2310,19,7,2079,231,rf-wv-5est,allround,1.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,sonar,208,60,2,187,21,rf-wv-5est,allround,15.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vehicle,846,18,4,761,85,rf-wv-5est,allround,25.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,votes,435,16,2,391,44,rf-wv-5est,allround,4.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,vowel,990,11,11,891,99,rf-wv-5est,allround,2.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,zip,9298,256,10,7291,2007,rf-wv-5est,allround,6.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,adult,5908,14,2,5317,591,rf-wv-5est,allround,14.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,audiology,226,69,24,203,23,rf-wv-5est,allround,22.1,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,banding,138,29,2,124,14,rf-wv-5est,allround,24,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,aus credit,690,15,2,621,69,rf-wv-5est,allround,12.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,heart,270,13,2,243,27,rf-wv-5est,allround,15.2,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,hepatitis,155,19,2,139,16,rf-wv-5est,allround,14.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,house,91,16,2,81,10,rf-wv-5est,allround,4.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,iris,150,4,3,135,15,rf-wv-5est,allround,4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,lymphoma,148,18,4,133,15,rf-wv-5est,allround,14.3,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,mushroom,8124,22,2,7311,813,rf-wv-5est,allround,0,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,post-opt,90,8,3,81,9,rf-wv-5est,allround,28.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,tumor,339,17,22,305,34,rf-wv-5est,allround,51.9,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,promotor,106,57,2,95,11,rf-wv-5est,allround,4.7,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,rheumatism,355,32,6,319,36,rf-wv-5est,allround,31,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,spam,4601,57,2,4140,461,rf-wv-5est,allround,4.8,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,splice,3190,60,3,2871,319,rf-wv-5est,allround,3.4,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Improving random forests,Robnik-Sikonja,2004,Machine Learning,yeast,1484,8,10,1335,149,rf-wv-5est,allround,38.6,NA,default,100,sqrt(p),10-fold cv,wilcoxon signed-rank
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,audiology,226,69,24,158,68,rf,allround,27.3,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,car,1728,6,4,1209,519,rf,allround,6.5,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,dna,106,57,2,74,32,rf,allround,9.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,glass,214,9,6,149,65,rf,allround,23.8,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,image,1000,2500,2,700,300,rf,allround,15,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,monk1,432,6,2,302,130,rf,allround,0.3,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity2,200,12,2,140,60,rf,allround,7.5,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity3,200,13,2,140,60,rf,allround,36.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,sonar,208,60,2,145,63,rf,allround,17,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,tic-tac,958,9,2,670,288,rf,allround,6.4,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,vehicle,846,18,4,592,254,rf,allround,25.4,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,zoo,101,16,7,70,31,rf,allround,10.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,audiology,226,69,24,158,68,rf-DVSheom,allround,25.9,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,car,1728,6,4,1209,519,rf-DVSheom,allround,6.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,dna,106,57,2,74,32,rf-DVSheom,allround,8.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,glass,214,9,6,149,65,rf-DVSheom,allround,23.5,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,image,1000,2500,2,700,300,rf-DVSheom,allround,14.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,monk1,432,6,2,302,130,rf-DVSheom,allround,0.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity2,200,12,2,140,60,rf-DVSheom,allround,2.7,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity3,200,13,2,140,60,rf-DVSheom,allround,28.4,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,sonar,208,60,2,145,63,rf-DVSheom,allround,15.9,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,tic-tac,958,9,2,670,288,rf-DVSheom,allround,4,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,vehicle,846,18,4,592,254,rf-DVSheom,allround,25.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,zoo,101,16,7,70,31,rf-DVSheom,allround,10.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,audiology,226,69,24,158,68,rf-DVSheomw,allround,26,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,car,1728,6,4,1209,519,rf-DVSheomw,allround,6.3,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,dna,106,57,2,74,32,rf-DVSheomw,allround,8.9,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,glass,214,9,6,149,65,rf-DVSheomw,allround,23.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,image,1000,2500,2,700,300,rf-DVSheomw,allround,14,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,monk1,432,6,2,302,130,rf-DVSheomw,allround,0,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity2,200,12,2,140,60,rf-DVSheomw,allround,2.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity3,200,13,2,140,60,rf-DVSheomw,allround,27.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,sonar,208,60,2,145,63,rf-DVSheomw,allround,16,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,tic-tac,958,9,2,670,288,rf-DVSheomw,allround,3.9,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,vehicle,846,18,4,592,254,rf-DVSheomw,allround,25.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,zoo,101,16,7,70,31,rf-DVSheomw,allround,9.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,audiology,226,69,24,158,68,rf-DVSrf,allround,26.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,car,1728,6,4,1209,519,rf-DVSrf,allround,6.4,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,dna,106,57,2,74,32,rf-DVSrf,allround,9.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,glass,214,9,6,149,65,rf-DVSrf,allround,23,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,image,1000,2500,2,700,300,rf-DVSrf,allround,14.3,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,monk1,432,6,2,302,130,rf-DVSrf,allround,0,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity2,200,12,2,140,60,rf-DVSrf,allround,2.2,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity3,200,13,2,140,60,rf-DVSrf,allround,28.7,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,sonar,208,60,2,145,63,rf-DVSrf,allround,16,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,tic-tac,958,9,2,670,288,rf-DVSrf,allround,3.9,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,vehicle,846,18,4,592,254,rf-DVSrf,allround,25.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,zoo,101,16,7,70,31,rf-DVSrf,allround,10.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,audiology,226,69,24,158,68,rf-DVSrfw,allround,26.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,car,1728,6,4,1209,519,rf-DVSrfw,allround,6.3,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,dna,106,57,2,74,32,rf-DVSrfw,allround,8.7,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,glass,214,9,6,149,65,rf-DVSrfw,allround,22.8,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,image,1000,2500,2,700,300,rf-DVSrfw,allround,14.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,monk1,432,6,2,302,130,rf-DVSrfw,allround,0,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity2,200,12,2,140,60,rf-DVSrfw,allround,2.3,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,parity3,200,13,2,140,60,rf-DVSrfw,allround,27.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,sonar,208,60,2,145,63,rf-DVSrfw,allround,15.6,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,tic-tac,958,9,2,670,288,rf-DVSrfw,allround,3.4,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,vehicle,846,18,4,592,254,rf-DVSrfw,allround,25.1,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Dynamic integration with random forests,Tsymbal et al.,2006,Machine Learning,zoo,101,16,7,70,31,rf-DVSrfw,allround,8.8,NA,default,100,log(p) + 1,30 run 70/30,paired t-test
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,orf-rnd,allround,27.3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ecoli,369,7,8,336,33,orf-rnd,allround,12.9,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,orf-rnd,allround,23.1,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,glass,235,9,6,214,21,orf-rnd,allround,24.4,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,orf-rnd,allround,3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,vehicle,930,18,4,846,84,orf-rnd,allround,23.1,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,image,"2,541",19,7,"2,310",231,orf-rnd,allround,1.6,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,orf-rnd,allround,13.6,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,letters,"20,000",16,26,"15,000","5,000",orf-rnd,allround,3.4,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,satellite,"6,435",36,6,"4,435","2,000",orf-rnd,allround,9.1,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,waveform,"3,300",21,3,300,"3,000",orf-rnd,allround,16,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",orf-rnd,allround,3.8,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",orf-rnd,allround,16.8,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",orf-rnd,allround,4.8,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,rf-rcp-k1,allround,27.3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ecoli,369,7,8,336,33,rf-rcp-k1,allround,13.7,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,rf-rcp-k1,allround,23.2,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,glass,235,9,6,214,21,rf-rcp-k1,allround,23.5,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,rf-rcp-k1,allround,3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,vehicle,930,18,4,846,84,rf-rcp-k1,allround,22.5,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,image,"2,541",19,7,"2,310",231,rf-rcp-k1,allround,1.6,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,rf-rcp-k1,allround,13.9,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,letters,"20,000",16,26,"15,000","5,000",rf-rcp-k1,allround,3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,satellite,"6,435",36,6,"4,435","2,000",rf-rcp-k1,allround,8.8,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,waveform,"3,300",21,3,300,"3,000",rf-rcp-k1,allround,15.9,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",rf-rcp-k1,allround,2.9,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",rf-rcp-k1,allround,15,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",rf-rcp-k1,allround,4.9,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,rf-rcp-k4,allround,26.8,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ecoli,369,7,8,336,33,rf-rcp-k4,allround,13.6,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,rf-rcp-k4,allround,22.4,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,glass,235,9,6,214,21,rf-rcp-k4,allround,24.3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,rf-rcp-k4,allround,2.7,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,vehicle,930,18,4,846,84,rf-rcp-k4,allround,23.2,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,image,"2,541",19,7,"2,310",231,rf-rcp-k4,allround,1.6,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,rf-rcp-k4,allround,14,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,letters,"20,000",16,26,"15,000","5,000",rf-rcp-k4,allround,3.3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,satellite,"6,435",36,6,"4,435","2,000",rf-rcp-k4,allround,9.2,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,waveform,"3,300",21,3,300,"3,000",rf-rcp-k4,allround,16,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",rf-rcp-k4,allround,3,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",rf-rcp-k4,allround,14.9,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",rf-rcp-k4,allround,4.5,NA,none,100,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,orf-rnd-k4,allround,3,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,orf-rnd-k4,allround,23.2,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,orf-rnd-k4,allround,13.9,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,orf-rnd-k4,allround,27.3,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",orf-rnd-k4,allround,2.9,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",orf-rnd-k4,allround,15,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",orf-rnd-k4,allround,4.9,NA,none,1000,8,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,adaboost,allround,3.6,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,adaboost,allround,22.6,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,adaboost,allround,16.1,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,adaboost,allround,27.6,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",adaboost,allround,5.4,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",adaboost,allround,19,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",adaboost,allround,6.2,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,logitboost,allround,3.4,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,logitboost,allround,21.7,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,logitboost,allround,13.7,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,logitboost,allround,26.6,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",logitboost,allround,5.4,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",logitboost,allround,18.9,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",logitboost,allround,6.1,NA,none,ntree-tuning,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,dann,allround,2.9,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,dann,allround,24.6,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,dann,allround,10.9,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,dann,allround,33.9,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",dann,allround,4.2,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",dann,allround,16.4,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",dann,allround,19.6,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,breast cancer,751,9,2,683,68,adamenn,allround,3.4,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,diabetes,585,7,2,532,53,adamenn,allround,23.1,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,sonar,228,60,2,208,20,adamenn,allround,14,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,liver,379,6,2,345,34,adamenn,allround,36.4,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,twonorm,"3,300",20,2,300,"3,000",adamenn,allround,3.3,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,threenorm,"3,300",20,2,300,"3,000",adamenn,allround,15.3,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Random forests and adaptive nearest neighbors,Lin and Jeon,2006,Journal of the American Statistical Association,ringnorm,"3,300",20,2,300,"3,000",adamenn,allround,18.9,NA,fixed/cv-5,NA,NA,100 runs 90/10,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,breast cancer alt,286,9,2,257,29,mml-rf,allround,26.6,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,breast cancer,699,9,2,629,70,mml-rf,allround,2.6,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,bupa,345,6,2,310,35,mml-rf,allround,32.1,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,cleveland heart,303,13,2,272,31,mml-rf,allround,16.6,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,ecoli,336,7,8,302,34,mml-rf,allround,14.1,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,german credit,1000,24,2,900,100,mml-rf,allround,25.9,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,glass,214,9,6,192,22,mml-rf,allround,32.9,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,image,2310,19,7,2079,231,mml-rf,allround,8,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,ionosphere,351,34,2,315,36,mml-rf,allround,6.2,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,pima,768,8,2,691,77,mml-rf,allround,23.8,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,satellite,6435,36,6,5791,644,mml-rf,allround,16.7,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,sonar,208,60,2,187,21,mml-rf,allround,18.1,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,vowel,990,10,11,891,99,mml-rf,allround,35.7,NA,none,1000,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,breast cancer alt,286,9,2,257,29,adaboost,allround,27.1,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,breast cancer,699,9,2,629,70,adaboost,allround,3.7,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,bupa,345,6,2,310,35,adaboost,allround,31.5,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,cleveland heart,303,13,2,272,31,adaboost,allround,19.4,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,ecoli,336,7,8,302,34,adaboost,allround,16,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,german credit,1000,24,2,900,100,adaboost,allround,24.6,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,glass,214,9,6,192,22,adaboost,allround,25.1,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,image,2310,19,7,2079,231,adaboost,allround,2.1,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,ionosphere,351,34,2,315,36,adaboost,allround,6.4,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,pima,768,8,2,691,77,adaboost,allround,24.7,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,satellite,6435,36,6,5791,644,adaboost,allround,9.3,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,sonar,208,60,2,187,21,adaboost,allround,19.2,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,vowel,990,10,11,891,99,adaboost,allround,10.1,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,breast cancer alt,286,9,2,257,29,rf,allround,29,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,breast cancer,699,9,2,629,70,rf,allround,3.3,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,bupa,345,6,2,310,35,rf,allround,27,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,cleveland heart,303,13,2,272,31,rf,allround,17.5,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,ecoli,336,7,8,302,34,rf,allround,13.8,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,german credit,1000,24,2,900,100,rf,allround,22.9,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,glass,214,9,6,192,22,rf,allround,19.2,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,image,2310,19,7,2079,231,rf,allround,1.9,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,ionosphere,351,34,2,315,36,rf,allround,7.2,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,pima,768,8,2,691,77,rf,allround,24.6,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,satellite,6435,36,6,5791,644,rf,allround,8.5,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,sonar,208,60,2,187,21,rf,allround,16,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
Decision forests with oblique decision trees,Tan and Dowe,2006,Advances in Artificial Intelligence,vowel,990,10,11,891,99,rf,allround,3,NA,none,ntree-tuning,NA,10 runs 10-cv,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,glass,214,9,6,192,22,adaboost,allround,22,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,breast cancer,699,9,2,629,70,adaboost,allround,3.2,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,pima,768,8,2,691,77,adaboost,allround,26.6,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,sonar,208,60,2,187,21,adaboost,allround,15.6,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vowel,990,10,11,891,99,adaboost,allround,4.1,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ionosphere,351,34,2,315,36,adaboost,allround,6.4,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vehicle,846,18,4,761,85,adaboost,allround,23.2,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,soybean,683,35,19,614,69,adaboost,allround,5.8,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,german credit,1000,24,2,900,100,adaboost,allround,23.5,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,image,2310,19,7,2079,231,adaboost,allround,1.6,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ecoli,336,7,8,302,34,adaboost,allround,14.8,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,votes,435,16,2,391,44,adaboost,allround,4.8,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,liver,345,6,2,310,35,adaboost,allround,30.7,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,letters,20000,16,26,15000,5000,adaboost,allround,3.4,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,satellite,6435,36,6,4435,2000,adaboost,allround,8.8,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,zip,9298,256,10,7291,2007,adaboost,allround,6.2,NA,none,100,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,waveform,3300,21,3,300,3000,adaboost,allround,17.8,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,twonorm,3300,20,2,300,3000,adaboost,allround,4.9,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,threenorm,3300,20,2,300,3000,adaboost,allround,18.8,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ringnorm,3300,20,2,300,3000,adaboost,allround,6.9,NA,none,50,NA,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,glass,214,9,6,192,22,rf,allround,20.6,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,breast cancer,699,9,2,629,70,rf,allround,2.9,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,pima,768,8,2,691,77,rf,allround,24.2,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,sonar,208,60,2,187,21,rf,allround,15.9,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vowel,990,10,11,891,99,rf,allround,3.4,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ionosphere,351,34,2,315,36,rf,allround,7.1,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vehicle,846,18,4,761,85,rf,allround,25.8,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,soybean,683,35,19,614,69,rf,allround,6,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,german credit,1000,24,2,900,100,rf,allround,24.4,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,image,2310,19,7,2079,231,rf,allround,2.1,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ecoli,336,7,8,302,34,rf,allround,12.8,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,votes,435,16,2,391,44,rf,allround,4.1,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,liver,345,6,2,310,35,rf,allround,25.1,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,letters,20000,16,26,15000,5000,rf,allround,3.5,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,satellite,6435,36,6,4435,2000,rf,allround,8.6,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,zip,9298,256,10,7291,2007,rf,allround,6.3,NA,default,200,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,waveform,3300,21,3,300,3000,rf,allround,17.2,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,twonorm,3300,20,2,300,3000,rf,allround,3.9,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,threenorm,3300,20,2,300,3000,rf,allround,17.5,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ringnorm,3300,20,2,300,3000,rf,allround,4.9,NA,default,100,log(p) + 1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,glass,214,9,6,192,22,rf,allround,21.2,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,breast cancer,699,9,2,629,70,rf,allround,2.7,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,pima,768,8,2,691,77,rf,allround,24.3,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,sonar,208,60,2,187,21,rf,allround,18,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vowel,990,10,11,891,99,rf,allround,3.3,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ionosphere,351,34,2,315,36,rf,allround,7.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vehicle,846,18,4,761,85,rf,allround,26.4,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,soybean,683,35,19,614,69,rf,allround,6.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,german credit,1000,24,2,900,100,rf,allround,26.2,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,image,2310,19,7,2079,231,rf,allround,2.7,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ecoli,336,7,8,302,34,rf,allround,13,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,votes,435,16,2,391,44,rf,allround,4.6,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,liver,345,6,2,310,35,rf,allround,24.7,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,letters,20000,16,26,15000,5000,rf,allround,4.7,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,satellite,6435,36,6,4435,2000,rf,allround,10.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,zip,9298,256,10,7291,2007,rf,allround,7.8,NA,none,200,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,waveform,3300,21,3,300,3000,rf,allround,17.3,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,twonorm,3300,20,2,300,3000,rf,allround,3.9,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,threenorm,3300,20,2,300,3000,rf,allround,17.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ringnorm,3300,20,2,300,3000,rf,allround,4.9,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,glass,214,9,6,192,22,pert,allround,21.4,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,breast cancer,699,9,2,629,70,pert,allround,2.8,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,pima,768,8,2,691,77,pert,allround,24.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,sonar,208,60,2,187,21,pert,allround,16.2,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vowel,990,10,11,891,99,pert,allround,2.3,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ionosphere,351,34,2,315,36,pert,allround,6.8,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,vehicle,846,18,4,761,85,pert,allround,27.4,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,soybean,683,35,19,614,69,pert,allround,6.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,german credit,1000,24,2,900,100,pert,allround,24.9,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,image,2310,19,7,2079,231,pert,allround,2.6,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ecoli,336,7,8,302,34,pert,allround,14.5,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,votes,435,16,2,391,44,pert,allround,4.4,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,liver,345,6,2,310,35,pert,allround,27.9,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,letters,20000,16,26,15000,5000,pert,allround,4.4,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,satellite,6435,36,6,4435,2000,pert,allround,10.2,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,zip,9298,256,10,7291,2007,pert,allround,7.6,NA,none,200,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,waveform,3300,21,3,300,3000,pert,allround,17.8,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,twonorm,3300,20,2,300,3000,pert,allround,3.6,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,threenorm,3300,20,2,300,3000,pert,allround,17.6,NA,none,100,1,500 runs 90/10,simple diff
PERT - perfect random tree ensembles,Cutler and Zhao,2001,Computing Science and Statistics,ringnorm,3300,20,2,300,3000,pert,allround,12.8,NA,none,100,1,500 runs 90/10,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,no info,HD-baseline,28.9,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,no info,HD-baseline,42.9,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,no info,HD-baseline,53.7,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,no info,HD-baseline,85.2,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,no info,HD-baseline,15.8,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,no info,HD-baseline,76.2,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,no info,HD-baseline,35.5,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,no info,HD-baseline,32.3,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,no info,HD-baseline,49,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,no info,HD-baseline,63.5,NA,NA,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,svm-linear,HD,1.4,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,svm-linear,HD,32.5,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,svm-linear,HD,38,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,svm-linear,HD,25.6,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,svm-linear,HD,20.3,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,svm-linear,HD,13.8,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,svm-linear,HD,14.7,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,svm-linear,HD,1,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,svm-linear,HD,6.4,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,svm-linear,HD,1.7,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,knn,HD,2.9,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,knn,HD,33.7,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,knn,HD,44.9,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,knn,HD,31.7,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,knn,HD,17.4,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,knn,HD,17.4,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,knn,HD,15.2,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,knn,HD,0.8,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,knn,HD,10,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,knn,HD,2.3,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,dlda,HD,2,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,dlda,HD,33.1,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,dlda,HD,37,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,dlda,HD,28.6,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,dlda,HD,19.4,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,dlda,HD,18.3,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,dlda,HD,13.7,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,dlda,HD,2.1,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,dlda,HD,14.9,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,dlda,HD,1.1,NA,pretuned,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,shrunkCent.l,select-HD,2.5,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,shrunkCent.l,select-HD,32.4,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,shrunkCent.l,select-HD,39.6,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,shrunkCent.l,select-HD,25.6,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,shrunkCent.l,select-HD,17.7,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,shrunkCent.l,select-HD,16.3,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,shrunkCent.l,select-HD,12.3,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,shrunkCent.l,select-HD,2.8,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,shrunkCent.l,select-HD,8.8,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,shrunkCent.l,select-HD,1.2,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,shrunkCent.s,select-HD,6.2,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,shrunkCent.s,select-HD,32.6,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,shrunkCent.s,select-HD,40.1,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,shrunkCent.s,select-HD,24.6,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,shrunkCent.s,select-HD,17.9,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,shrunkCent.s,select-HD,15.9,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,shrunkCent.s,select-HD,12.2,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,shrunkCent.s,select-HD,3.3,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,shrunkCent.s,select-HD,8.9,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,shrunkCent.s,select-HD,2.5,NA,none,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,nnVarSel,select-HD,5.6,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,nnVarSel,select-HD,33.7,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,nnVarSel,select-HD,42.4,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,nnVarSel,select-HD,23.7,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,nnVarSel,select-HD,18.1,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,nnVarSel,select-HD,19.4,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,nnVarSel,select-HD,15.8,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,nnVarSel,select-HD,4,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,nnVarSel,select-HD,8.1,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,nnVarSel,select-HD,3.1,NA,cv,NA,NA,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,rf,select-HD,5.1,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,rf,select-HD,34.2,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,rf,select-HD,35.1,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,rf,select-HD,25.2,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,rf,select-HD,12.5,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,rf,select-HD,15.4,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,rf,select-HD,12.7,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,rf,select-HD,0.9,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,rf,select-HD,7.7,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,rf,select-HD,2.1,NA,default,5000,sqrt(p),200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,varSelRF.se0,select-HD,8.7,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,varSelRF.se0,select-HD,33.7,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,varSelRF.se0,select-HD,34.6,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,varSelRF.se0,select-HD,32.7,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,varSelRF.se0,select-HD,18.5,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,varSelRF.se0,select-HD,21.6,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,varSelRF.se0,select-HD,15.9,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,varSelRF.se0,select-HD,4.7,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,varSelRF.se0,select-HD,6.1,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,varSelRF.se0,select-HD,3.9,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,leukemia,38,3051,2,23,15,varSelRF.se1,select-HD,7.5,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.2.class,77,4869,2,48,29,varSelRF.se1,select-HD,33.2,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,breast.3.class,95,4869,3,59,36,varSelRF.se1,select-HD,36.4,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,nci60,61,5244,8,38,23,varSelRF.se1,select-HD,35.3,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,adenocarcinoma,76,9868,2,47,29,varSelRF.se1,select-HD,20.7,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,brain,42,5597,5,26,16,varSelRF.se1,select-HD,21.6,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,colon,62,2000,2,39,23,varSelRF.se1,select-HD,17.7,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,lymphoma,62,4026,3,39,23,varSelRF.se1,select-HD,4.2,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,prostate,102,6033,2,64,38,varSelRF.se1,select-HD,6.4,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Gene selection and classification of microarray data using random forests,Diaz-Uriarte and Andres,2006,Bioinformatics,srbct,63,2308,4,39,24,varSelRF.se1,select-HD,3.8,NA,fixed,2000,1,200 runs 0.632+ boot,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,rf,select-HD,58.3,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,rf,select-HD,8.3,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,rf,select-HD,75,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,rf,select-HD,58.3,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,rf,select-HD,13.2,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,rf,select-HD,21.4,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,rf,select-HD,2.9,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,rf,select-HD,54.3,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,rf,select-HD,15.4,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,rf,select-HD,35.7,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,rf,select-HD,0,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,rf,select-HD,20,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,rf-pvalFilter,select-HD,58.3,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,rf-pvalFilter,select-HD,8.3,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,rf-pvalFilter,select-HD,75,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,rf-pvalFilter,select-HD,66.7,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,rf-pvalFilter,select-HD,14.5,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,rf-pvalFilter,select-HD,7.1,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,rf-pvalFilter,select-HD,2.9,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,rf-pvalFilter,select-HD,51.4,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,rf-pvalFilter,select-HD,7.7,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,rf-pvalFilter,select-HD,42.9,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,rf-pvalFilter,select-HD,0,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,rf-pvalFilter,select-HD,20,NA,default,1000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,rf,select-HD,25,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,rf,select-HD,8.3,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,rf,select-HD,33.3,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,rf,select-HD,58.3,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,rf,select-HD,14.5,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,rf,select-HD,0,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,rf,select-HD,2.9,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,rf,select-HD,28.6,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,rf,select-HD,0,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,rf,select-HD,35.7,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,rf,select-HD,0,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,rf,select-HD,30,NA,fixed,1000,p^(3/4),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,varSelRFboot,select-HD,52.7,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,varSelRFboot,select-HD,26,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,varSelRFboot,select-HD,53.5,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,varSelRFboot,select-HD,49.4,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,varSelRFboot,select-HD,21.1,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,varSelRFboot,select-HD,22.9,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,varSelRFboot,select-HD,4.1,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,varSelRFboot,select-HD,38.7,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,varSelRFboot,select-HD,30.6,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,varSelRFboot,select-HD,28.3,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,varSelRFboot,select-HD,5.6,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,varSelRFboot,select-HD,29.5,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,varSelRFcv,select-HD,54.3,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,varSelRFcv,select-HD,31.6,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,varSelRFcv,select-HD,55.6,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,varSelRFcv,select-HD,49.6,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,varSelRFcv,select-HD,22.9,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,varSelRFcv,select-HD,28.4,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,varSelRFcv,select-HD,6.1,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,varSelRFcv,select-HD,42.2,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,varSelRFcv,select-HD,35.7,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,varSelRFcv,select-HD,33.5,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,varSelRFcv,select-HD,8.3,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,varSelRFcv,select-HD,34.8,NA,fixed,2000,1,OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,enrichRF(t),select-HD,16.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,enrichRF(t),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,enrichRF(t),select-HD,83.3,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,enrichRF(t),select-HD,66.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,enrichRF(t),select-HD,14.5,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,enrichRF(t),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,enrichRF(t),select-HD,2.9,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,enrichRF(t),select-HD,45.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,enrichRF(t),select-HD,15.4,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,enrichRF(t),select-HD,25,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,enrichRF(t),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,enrichRF(t),select-HD,10,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,enrichRF(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,enrichRF(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,enrichRF(Ct),select-HD,66.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,enrichRF(Ct),select-HD,66.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,enrichRF(Ct),select-HD,14.5,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,enrichRF(Ct),select-HD,7.1,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,enrichRF(Ct),select-HD,2.9,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,enrichRF(Ct),select-HD,54.3,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,enrichRF(Ct),select-HD,15.4,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,enrichRF(Ct),select-HD,25,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,enrichRF(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,enrichRF(Ct),select-HD,10,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,enrichRFcv(t),select-HD,8.3,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,enrichRFcv(t),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,enrichRFcv(t),select-HD,75,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,enrichRFcv(t),select-HD,41.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,enrichRFcv(t),select-HD,14.5,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,enrichRFcv(t),select-HD,7.1,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,enrichRFcv(t),select-HD,2.9,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,enrichRFcv(t),select-HD,48.6,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,enrichRFcv(t),select-HD,7.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,enrichRFcv(t),select-HD,25,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,enrichRFcv(t),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,enrichRFcv(t),select-HD,10,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0,12,45101,2,7,5,enrichRFcv(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18,12,45101,2,7,5,enrichRFcv(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day0Scrambled,12,45101,2,7,5,enrichRFcv(Ct),select-HD,66.7,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,slc17a5Day18Scrambled,12,45101,2,7,5,enrichRFcv(Ct),select-HD,58.3,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,adenocarcinoma microArray,76,13432,2,47,29,enrichRFcv(Ct),select-HD,15.8,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,astrocytoma,14,12625,2,8,6,enrichRFcv(Ct),select-HD,7.1,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,breast cancer microArray,35,15926,2,22,13,enrichRFcv(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,pima microArray,35,22283,2,22,13,enrichRFcv(Ct),select-HD,57.1,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,epilepsy,13,31099,2,8,5,enrichRFcv(Ct),select-HD,15.4,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,hiv,28,12625,2,17,11,enrichRFcv(Ct),select-HD,28.6,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,lymph node sinus,20,22283,2,12,8,enrichRFcv(Ct),select-HD,0,NA,default,2000,sqrt(p),OOB,simple diff
Enriched random forests,Amaratunga et al.,2008,Bioinformatics,prostate microArray,10,54675,2,6,4,enrichRFcv(Ct),select-HD,10,NA,default,2000,sqrt(p),OOB,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,mushroom,8124,112,2,6000,2124,rf,allround,1,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,dna,2586,180,3,1400,1186,rf,allround,10.9,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,satellite,5104,36,6,3104,2000,rf,allround,11.3,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,zip,9298,256,10,7291,2007,rf,allround,7.8,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,letters,20000,16,26,15000,5000,rf,allround,9.7,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,mushroom,8124,112,2,6000,2124,online-rf,online,1.2,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,dna,2586,180,3,1400,1186,online-rf,online,11.2,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,satellite,5104,36,6,3104,2000,online-rf,online,11.8,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,zip,9298,256,10,7291,2007,online-rf,online,8.6,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,letters,20000,16,26,15000,5000,online-rf,online,10.4,NA,NA,200,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,mushroom,8124,112,2,6000,2124,online-adaboost,online,1.3,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,dna,2586,180,3,1400,1186,online-adaboost,online,17.3,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,satellite,5104,36,6,3104,2000,online-adaboost,online,25.7,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,zip,9298,256,10,7291,2007,online-adaboost,online,22.4,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,letters,20000,16,26,15000,5000,online-adaboost,online,26.3,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,mushroom,8124,112,2,6000,2124,online-logitboost,online,1.2,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,dna,2586,180,3,1400,1186,online-logitboost,online,11.7,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,satellite,5104,36,6,3104,2000,online-logitboost,online,15.2,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,zip,9298,256,10,7291,2007,online-logitboost,online,13.4,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,letters,20000,16,26,15000,5000,online-logitboost,online,22.3,NA,NA,500,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,mushroom,8124,112,2,6000,2124,online-savage,online,0.013,NA,NA,NA,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,dna,2586,180,3,1400,1186,online-savage,online,0.097,NA,NA,NA,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,satellite,5104,36,6,3104,2000,online-savage,online,0.156,NA,NA,NA,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,zip,9298,256,10,7291,2007,online-savage,online,0.139,NA,NA,NA,NA,NA,simple diff
On-line random forests,Saffari et al.,2009,ICCV Workshops,letters,20000,16,26,15000,5000,online-savage,online,0.241,NA,NA,NA,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,breast cancer,699,9,2,461,238,rf,allround,30.77,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,cleveland heart,303,13,2,199,104,rf,allround,17.31,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,glass,214,9,6,141,73,rf,allround,30.14,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,hepatitis,155,19,2,102,53,rf,allround,15.1,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,labour,57,16,2,37,20,rf,allround,10,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,sonar,208,60,2,137,71,rf,allround,26.77,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,vowel,990,21,11,653,337,rf,allround,11.87,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,letters,20000,16,26,13200,6800,rf,allround,6.86,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,waveform,300,21,3,198,102,rf,allround,19.18,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,breast cancer,699,9,2,461,238,baggedRF,allround,26.54,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,cleveland heart,303,13,2,199,104,baggedRF,allround,15.39,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,glass,214,9,6,141,73,baggedRF,allround,27.4,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,hepatitis,155,19,2,102,53,baggedRF,allround,15.1,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,labour,57,16,2,37,20,baggedRF,allround,5,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,sonar,208,60,2,137,71,baggedRF,allround,19.72,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,vowel,990,21,11,653,337,baggedRF,allround,8.91,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,letters,20000,16,26,13200,6800,baggedRF,allround,6.86,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,waveform,300,21,3,198,102,baggedRF,allround,15.24,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,breast cancer,699,9,2,461,238,boostedRF,allround,33.68,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,cleveland heart,303,13,2,199,104,boostedRF,allround,17.31,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,glass,214,9,6,141,73,boostedRF,allround,32.88,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,hepatitis,155,19,2,102,53,boostedRF,allround,11.33,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,labour,57,16,2,37,20,boostedRF,allround,5,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,sonar,208,60,2,137,71,boostedRF,allround,26.77,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,vowel,990,21,11,653,337,boostedRF,allround,7.72,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,letters,20000,16,26,13200,6800,boostedRF,allround,6.86,NA,NA,20,NA,NA,simple diff
Meta random forests,Boinee et al.,2008,International Journal of Computationnal Intelligence,waveform,300,21,3,198,102,boostedRF,allround,16.95,NA,NA,20,NA,NA,simple diff
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,svm-radial,allround,2.8,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,svm-radial,allround,15.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,svm-radial,allround,23,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,svm-radial,allround,20.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,svm-radial,allround,3.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,svm-radial,allround,17.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,svm-radial,allround,12,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,svm-radial,allround,21.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,svm-radial,allround,9.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,svm-radial,allround,4.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,svm-radial,allround,5.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,svm-radial,allround,17.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,svm-radial,allround,10.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,svm-radial,allround,30.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,svm-radial,allround,30.7,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,svm-radial,allround,12.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,svm-radial,allround,5.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,svm-radial,allround,13.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,svm-radial,allround,2.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,svm-radial,allround,2.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,svm-radial,allround,1.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,svm-radial,allround,2.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,svm-radial,allround,7.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,svm-radial,allround,2.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,svm-radial,allround,25.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,svm-radial,allround,6.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,svm-radial,allround,11.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,svm-radial,allround,19.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,svm-radial,allround,22.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,svm-radial,allround,27.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,svm-radial,allround,27.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,svm-radial,allround,4.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,svm-radial,allround,2.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,svm-radial,allround,3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,svm-radial,allround,9.7,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,svm-radial,allround,5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,svm-radial,allround,27.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,svm-radial,allround,25.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,svm-radial,allround,17.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,svm-radial,allround,18,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,knn,allround,5.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,knn,allround,32,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,knn,allround,35.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,knn,allround,21.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,knn,allround,1.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,knn,allround,20.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,knn,allround,16.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,knn,allround,23.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,knn,allround,12.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,knn,allround,3.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,knn,allround,14,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,knn,allround,18.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,knn,allround,14.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,knn,allround,32,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,knn,allround,31.7,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,knn,allround,17,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,knn,allround,5.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,knn,allround,14.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,knn,allround,1.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,knn,allround,2.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,knn,allround,0.4,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,knn,allround,3.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,knn,allround,7.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,knn,allround,3.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,knn,allround,29.8,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,knn,allround,6.7,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,knn,allround,12,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,knn,allround,19.7,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,knn,allround,23.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,knn,allround,42.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,knn,allround,40.5,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,knn,allround,9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,knn,allround,3.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,knn,allround,4.9,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,knn,allround,11,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,knn,allround,7.1,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,knn,allround,40.7,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,knn,allround,40.6,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,knn,allround,40.3,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,knn,allround,38.2,NA,3-cv grid search,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,tree-cart,allround,2.4,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,tree-cart,allround,14.8,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,tree-cart,allround,21.3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,tree-cart,allround,20.3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,tree-cart,allround,1.1,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,tree-cart,allround,30.4,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,tree-cart,allround,26.5,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,tree-cart,allround,23,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,tree-cart,allround,4.4,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,tree-cart,allround,6.2,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,tree-cart,allround,10.5,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,tree-cart,allround,27.2,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,tree-cart,allround,19.6,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,tree-cart,allround,34.1,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,tree-cart,allround,34.2,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,tree-cart,allround,19.8,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,tree-cart,allround,10.3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,tree-cart,allround,33.8,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,tree-cart,allround,21.6,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,tree-cart,allround,5.1,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,tree-cart,allround,5,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,tree-cart,allround,6,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,tree-cart,allround,15.8,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,tree-cart,allround,3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,tree-cart,allround,32.7,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,tree-cart,allround,18.1,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,tree-cart,allround,17.5,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,tree-cart,allround,22.3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,tree-cart,allround,25.2,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,tree-cart,allround,24.1,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,tree-cart,allround,25.2,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,tree-cart,allround,14.5,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,tree-cart,allround,9,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,tree-cart,allround,8.7,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,tree-cart,allround,15.7,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,tree-cart,allround,8,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,tree-cart,allround,26,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,tree-cart,allround,30.3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,tree-cart,allround,14.7,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,tree-cart,allround,15.3,NA,10-cv,NA,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,adaboost,allround,0.6,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,adaboost,allround,12.9,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,adaboost,allround,18.3,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,adaboost,allround,24.4,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,adaboost,allround,1.2,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,adaboost,allround,9.6,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,adaboost,allround,0,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,adaboost,allround,21.3,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,adaboost,allround,4.5,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,adaboost,allround,3.7,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,adaboost,allround,6.4,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,adaboost,allround,13.1,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,adaboost,allround,8.8,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,adaboost,allround,26.8,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,adaboost,allround,26.6,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,adaboost,allround,13.2,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,adaboost,allround,6.1,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,adaboost,allround,15.3,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,adaboost,allround,3.4,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,adaboost,allround,2.4,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,adaboost,allround,2.4,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,adaboost,allround,3.1,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,adaboost,allround,9.1,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,adaboost,allround,0.8,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,adaboost,allround,22.9,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,adaboost,allround,6.7,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,adaboost,allround,10.6,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,adaboost,allround,18.9,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,adaboost,allround,22.1,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,adaboost,allround,25.9,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,adaboost,allround,33.5,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,adaboost,allround,7.8,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,adaboost,allround,8.3,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,adaboost,allround,7.2,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,adaboost,allround,15.8,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,adaboost,allround,7.4,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,adaboost,allround,22.1,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,adaboost,allround,21.8,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,adaboost,allround,18,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,adaboost,allround,12.5,NA,3-cv grid search,ntree-tuning,NA,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,rf,allround,1.4,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,rf,allround,12.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,rf,allround,17.8,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,rf,allround,17.8,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,rf,allround,2.4,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,rf,allround,11,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,rf,allround,4.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,rf,allround,22.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,rf,allround,4.2,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,rf,allround,3.3,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,rf,allround,6.5,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,rf,allround,16.1,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,rf,allround,10.6,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,rf,allround,26.8,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,rf,allround,26.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,rf,allround,17.5,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,rf,allround,6,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,rf,allround,16.4,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,rf,allround,3.5,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,rf,allround,2.8,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,rf,allround,1.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,rf,allround,3.2,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,rf,allround,7.8,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,rf,allround,0.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,rf,allround,24.9,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,rf,allround,7.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,rf,allround,10.4,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,rf,allround,19,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,rf,allround,20.5,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,rf,allround,25.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,rf,allround,24.6,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,rf,allround,7.3,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,rf,allround,3.9,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,rf,allround,5,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,rf,allround,12,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,rf,allround,6.4,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,rf,allround,21.7,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,rf,allround,21.1,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,rf,allround,11.1,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,rf,allround,10.3,NA,default,ntree-tuning,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,rf-rnd,allround,52.2,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,rf-rnd,allround,17.9,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,rf-rnd,allround,40.6,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,rf-rnd,allround,18.7,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,rf-rnd,allround,43.3,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,rf-rnd,allround,50.1,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,rf-rnd,allround,65.3,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,rf-rnd,allround,30.2,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,rf-rnd,allround,7.7,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,rf-rnd,allround,3,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,rf-rnd,allround,7.5,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,rf-rnd,allround,16.3,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,rf-rnd,allround,17.9,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,rf-rnd,allround,27.8,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,rf-rnd,allround,27,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,rf-rnd,allround,17.9,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,rf-rnd,allround,6.1,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,rf-rnd,allround,15.1,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,rf-rnd,allround,2.8,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,rf-rnd,allround,3.3,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,rf-rnd,allround,50,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,rf-rnd,allround,50,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,rf-rnd,allround,50,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,rf-rnd,allround,1.7,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,rf-rnd,allround,27.5,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,rf-rnd,allround,9.3,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,rf-rnd,allround,12.2,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,rf-rnd,allround,19.1,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,rf-rnd,allround,23,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,rf-rnd,allround,29.6,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,rf-rnd,allround,30.8,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,rf-rnd,allround,8.9,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,rf-rnd,allround,4.4,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,rf-rnd,allround,5.2,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,rf-rnd,allround,14.1,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,rf-rnd,allround,6.4,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,rf-rnd,allround,26.4,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,rf-rnd,allround,25.4,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,rf-rnd,allround,21.9,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,rf-rnd,allround,22.1,NA,none,ntree-tuning,1,10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,orf-rnd,allround,23.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,orf-rnd,allround,15.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,orf-rnd,allround,20,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,orf-rnd,allround,19.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,orf-rnd,allround,5.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,orf-rnd,allround,20.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,orf-rnd,allround,12.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,orf-rnd,allround,26.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,orf-rnd,allround,5.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,orf-rnd,allround,2.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,orf-rnd,allround,5.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,orf-rnd,allround,14.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,orf-rnd,allround,10.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,orf-rnd,allround,27.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,orf-rnd,allround,27.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,orf-rnd,allround,18,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,orf-rnd,allround,5.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,orf-rnd,allround,14,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,orf-rnd,allround,2.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,orf-rnd,allround,1.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,orf-rnd,allround,1.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,orf-rnd,allround,4.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,orf-rnd,allround,9.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,orf-rnd,allround,1.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,orf-rnd,allround,25.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,orf-rnd,allround,8.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,orf-rnd,allround,10.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,orf-rnd,allround,19.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,orf-rnd,allround,23,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,orf-rnd,allround,34.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,orf-rnd,allround,35.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,orf-rnd,allround,7.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,orf-rnd,allround,3.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,orf-rnd,allround,4.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,orf-rnd,allround,13.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,orf-rnd,allround,6.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,orf-rnd,allround,29.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,orf-rnd,allround,32.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,orf-rnd,allround,25.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,orf-rnd,allround,29.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,orf-lda,allround,4.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,orf-lda,allround,13,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,orf-lda,allround,18,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,orf-lda,allround,19.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,orf-lda,allround,3.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,orf-lda,allround,16.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,orf-lda,allround,15.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,orf-lda,allround,22,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,orf-lda,allround,5.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,orf-lda,allround,3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,orf-lda,allround,5.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,orf-lda,allround,18,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,orf-lda,allround,10.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,orf-lda,allround,25.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,orf-lda,allround,26.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,orf-lda,allround,18,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,orf-lda,allround,5.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,orf-lda,allround,14.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,orf-lda,allround,1.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,orf-lda,allround,1.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,orf-lda,allround,2.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,orf-lda,allround,4.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,orf-lda,allround,16.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,orf-lda,allround,0.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,orf-lda,allround,19.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,orf-lda,allround,6.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,orf-lda,allround,10.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,orf-lda,allround,19.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,orf-lda,allround,13.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,orf-lda,allround,15,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,orf-lda,allround,14.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,orf-lda,allround,4.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,orf-lda,allround,3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,orf-lda,allround,3.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,orf-lda,allround,6.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,orf-lda,allround,3.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,orf-lda,allround,21.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,orf-lda,allround,25.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,orf-lda,allround,8.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,orf-lda,allround,11.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,chess,3196,36,2,NA,NA,orf-ridge,allround,2.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,credit,690,15,2,NA,NA,orf-ridge,allround,44.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,heart,270,13,2,NA,NA,orf-ridge,allround,17.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,hepatitis,155,19,2,NA,NA,orf-ridge,allround,20.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,monk3,432,8,2,NA,NA,orf-ridge,allround,1.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,promotergene,NA,NA,2,NA,NA,orf-ridge,allround,18.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tic-tac,958,9,2,NA,NA,orf-ridge,allround,14.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,titanic,2224,11,2,NA,NA,orf-ridge,allround,24.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,votes,435,16,2,NA,NA,orf-ridge,allround,5.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,breast cancer,699,9,2,NA,NA,orf-ridge,allround,3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ionosphere,351,34,2,NA,NA,orf-ridge,allround,5.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,sonar,208,60,2,NA,NA,orf-ridge,allround,18.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,musk,476,166,2,NA,NA,orf-ridge,allround,10.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,liver,345,6,2,NA,NA,orf-ridge,allround,26.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,pima,768,8,2,NA,NA,orf-ridge,allround,26.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,ringnorm,3300,20,2,NA,NA,orf-ridge,allround,18,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,spirals,300,2,2,NA,NA,orf-ridge,allround,5.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,threenorm,3300,20,2,NA,NA,orf-ridge,allround,14.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,twonorm,3300,20,2,NA,NA,orf-ridge,allround,1.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,circles,NA,NA,2,NA,NA,orf-ridge,allround,1.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits2-4,NA,NA,2,NA,NA,orf-ridge,allround,2.7,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits3-8,NA,NA,2,NA,NA,orf-ridge,allround,5.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,digits even-odd,NA,NA,2,NA,NA,orf-ridge,allround,16.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs srtm,NA,NA,2,NA,NA,orf-ridge,allround,1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,rs aster,NA,NA,2,NA,NA,orf-ridge,allround,19.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,quality,NA,NA,2,NA,NA,orf-ridge,allround,6.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor1,NA,NA,2,NA,NA,orf-ridge,allround,11,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,tumor2,NA,NA,2,NA,NA,orf-ridge,allround,20.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse1,NA,NA,2,NA,NA,orf-ridge,allround,13.4,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse2,NA,NA,2,NA,NA,orf-ridge,allround,15.5,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,bse3,NA,NA,2,NA,NA,orf-ridge,allround,12.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast1,NA,NA,2,NA,NA,orf-ridge,allround,4.3,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast2,NA,NA,2,NA,NA,orf-ridge,allround,2.8,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast3,NA,NA,2,NA,NA,orf-ridge,allround,3.2,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast4,NA,NA,2,NA,NA,orf-ridge,allround,5.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,yeast5,NA,NA,2,NA,NA,orf-ridge,allround,3.9,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 1,NA,NA,2,NA,NA,orf-ridge,allround,21.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine origin 2,NA,NA,2,NA,NA,orf-ridge,allround,22.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 1,NA,NA,2,NA,NA,orf-ridge,allround,4.6,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
On oblique random forests,Menze et al.,2011,Machine Learning and Knowledge Discovery in Databases,wine grape 2,NA,NA,2,NA,NA,orf-ridge,allround,11.1,NA,default,300,sqrt(p),10 runs 10-cv,paired cox-wilcoxon
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,pima,768,8,2,512,256,garf,allround,21.4844,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,glass,214,9,6,143,71,garf,allround,28.169,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ionosphere,351,34,2,235,116,garf,allround,4.3104,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,iris,150,4,3,100,50,garf,allround,4,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,labor,57,16,2,39,18,garf,allround,5.5556,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,soybean,683,35,19,456,227,garf,allround,14.5374,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,votes,435,16,2,290,145,garf,allround,3.4483,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,german credit,1000,24,2,667,333,garf,allround,26.1261,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ecoli,336,7,8,223,113,garf,allround,28.3186,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,letters,20000,16,26,13333,6667,garf,allround,15.9892,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,liver,345,6,2,229,116,garf,allround,30.1724,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,sonar,208,60,2,139,69,garf,allround,11.5942,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vehicle,846,18,4,564,282,garf,allround,26.2411,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vowel,990,10,11,660,330,garf,allround,25.4545,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,waveform,5000,21,3,3333,1667,garf,allround,14.817,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,pima,768,8,2,512,256,rf,allround,23.4375,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,glass,214,9,6,143,71,rf,allround,32.3944,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ionosphere,351,34,2,235,116,rf,allround,6.8966,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,iris,150,4,3,100,50,rf,allround,8,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,labor,57,16,2,39,18,rf,allround,11.1111,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,soybean,683,35,19,456,227,rf,allround,18.5022,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,votes,435,16,2,290,145,rf,allround,4.1379,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,german credit,1000,24,2,667,333,rf,allround,27.6276,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ecoli,336,7,8,223,113,rf,allround,30.0885,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,letters,20000,16,26,13333,6667,rf,allround,16.6492,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,liver,345,6,2,229,116,rf,allround,34.4828,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,sonar,208,60,2,139,69,rf,allround,14.4928,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vehicle,846,18,4,564,282,rf,allround,29.078,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vowel,990,10,11,660,330,rf,allround,26.9697,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,waveform,5000,21,3,3333,1667,rf,allround,15.4769,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,pima,768,8,2,512,256,rf,allround,24.6094,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,glass,214,9,6,143,71,rf,allround,23.9437,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ionosphere,351,34,2,235,116,rf,allround,7.7586,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,iris,150,4,3,100,50,rf,allround,6,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,labor,57,16,2,39,18,rf,allround,22.2222,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,soybean,683,35,19,456,227,rf,allround,12.7753,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,votes,435,16,2,290,145,rf,allround,1.3793,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,german credit,1000,24,2,667,333,rf,allround,27.3273,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ecoli,336,7,8,223,113,rf,allround,30.9735,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,liver,345,6,2,229,116,rf,allround,31.0345,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,sonar,208,60,2,139,69,rf,allround,18.8406,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vehicle,846,18,4,564,282,rf,allround,25.1773,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vowel,990,10,11,660,330,rf,allround,20,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,waveform,5000,21,3,3333,1667,rf,allround,14.997,NA,default,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,pima,768,8,2,512,256,adaboost,allround,19.9219,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,glass,214,9,6,143,71,adaboost,allround,66.1972,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ionosphere,351,34,2,235,116,adaboost,allround,8.6207,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,iris,150,4,3,100,50,adaboost,allround,4,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,labor,57,16,2,39,18,adaboost,allround,16.6667,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,soybean,683,35,19,456,227,adaboost,allround,67.4009,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,votes,435,16,2,290,145,adaboost,allround,0.6897,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,german credit,1000,24,2,667,333,adaboost,allround,31.5315,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ecoli,336,7,8,223,113,adaboost,allround,75.2212,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,liver,345,6,2,229,116,adaboost,allround,40.5172,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,sonar,208,60,2,139,69,adaboost,allround,23.1884,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vehicle,846,18,4,564,282,adaboost,allround,61.3475,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vowel,990,10,11,660,330,adaboost,allround,84.2424,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,waveform,5000,21,3,3333,1667,adaboost,allround,26.9946,NA,NA,NA,sqrt(p),10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,pima,768,8,2,512,256,tree-c4.5,allround,23.4375,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,glass,214,9,6,143,71,tree-c4.5,allround,40.8451,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ionosphere,351,34,2,235,116,tree-c4.5,allround,6.8966,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,iris,150,4,3,100,50,tree-c4.5,allround,4,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,labor,57,16,2,39,18,tree-c4.5,allround,22.2222,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,soybean,683,35,19,456,227,tree-c4.5,allround,16.2996,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,votes,435,16,2,290,145,tree-c4.5,allround,2.7586,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,german credit,1000,24,2,667,333,tree-c4.5,allround,30.3303,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ecoli,336,7,8,223,113,tree-c4.5,allround,31.8584,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,liver,345,6,2,229,116,tree-c4.5,allround,38.7931,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,sonar,208,60,2,139,69,tree-c4.5,allround,23.1884,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vehicle,846,18,4,564,282,tree-c4.5,allround,34.0426,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vowel,990,10,11,660,330,tree-c4.5,allround,34.8485,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,waveform,5000,21,3,3333,1667,tree-c4.5,allround,25.8548,NA,NA,1,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,pima,768,8,2,512,256,svm,allround,20.7031,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,glass,214,9,6,143,71,svm,allround,52.1127,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ionosphere,351,34,2,235,116,svm,allround,8.6207,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,iris,150,4,3,100,50,svm,allround,10,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,labor,57,16,2,39,18,svm,allround,16.6667,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,votes,435,16,2,290,145,svm,allround,92.7586,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,german credit,1000,24,2,667,333,svm,allround,28.5285,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,ecoli,336,7,8,223,113,svm,allround,38.9381,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,liver,345,6,2,229,116,svm,allround,42.2414,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,sonar,208,60,2,139,69,svm,allround,15.942,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vehicle,846,18,4,564,282,svm,allround,33.6879,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,vowel,990,10,11,660,330,svm,allround,48.4848,NA,NA,NA,NA,10 runs train/test,simple diff
GARF: Towards self-optimised random forests,Bader-el-den and Gaber,2012,Neural Information Processing,waveform,5000,21,3,3333,1667,svm,allround,13.9172,NA,NA,NA,NA,10 runs train/test,simple diff
Guided random forest in the RRF package,Deng,2013,arXiv preprint,adenocarcinoma,76,9868,2,50,26,grf-rf,select-HD,15.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,brain,42,5597,5,27,15,grf-rf,select-HD,14.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.2.class,77,4869,2,50,27,grf-rf,select-HD,34.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.3.class,95,4869,3,62,33,grf-rf,select-HD,39.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,colon,62,2000,2,40,22,grf-rf,select-HD,14.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,leukemia,38,3051,2,25,13,grf-rf,select-HD,4.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,lymphoma,62,4026,3,40,22,grf-rf,select-HD,0.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,nci60,61,5244,8,40,21,grf-rf,select-HD,31.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,prostate,102,6033,2,67,35,grf-rf,select-HD,8.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,srbct,63,2308,4,41,22,grf-rf,select-HD,2.4,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,adenocarcinoma,76,9868,2,50,26,grf,select-HD,15.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,brain,42,5597,5,27,15,grf,select-HD,16.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.2.class,77,4869,2,50,27,grf,select-HD,34.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.3.class,95,4869,3,62,33,grf,select-HD,39.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,colon,62,2000,2,40,22,grf,select-HD,14.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,leukemia,38,3051,2,25,13,grf,select-HD,3.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,lymphoma,62,4026,3,40,22,grf,select-HD,0.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,nci60,61,5244,8,40,21,grf,select-HD,33.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,prostate,102,6033,2,67,35,grf,select-HD,9.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,srbct,63,2308,4,41,22,grf,select-HD,2.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,adenocarcinoma,76,9868,2,50,26,rf,select-HD,15.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,brain,42,5597,5,27,15,rf,select-HD,17.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.2.class,77,4869,2,50,27,rf,select-HD,36.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.3.class,95,4869,3,62,33,rf,select-HD,41.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,colon,62,2000,2,40,22,rf,select-HD,16.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,leukemia,38,3051,2,25,13,rf,select-HD,6.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,lymphoma,62,4026,3,40,22,rf,select-HD,0.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,nci60,61,5244,8,40,21,rf,select-HD,31.9,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,prostate,102,6033,2,67,35,rf,select-HD,10.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,srbct,63,2308,4,41,22,rf,select-HD,3.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,adenocarcinoma,76,9868,2,50,26,grrf,select-HD,18.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,brain,42,5597,5,27,15,grrf,select-HD,26.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.2.class,77,4869,2,50,27,grrf,select-HD,35.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.3.class,95,4869,3,62,33,grrf,select-HD,39.5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,colon,62,2000,2,40,22,grrf,select-HD,19.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,leukemia,38,3051,2,25,13,grrf,select-HD,10.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,lymphoma,62,4026,3,40,22,grrf,select-HD,9.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,nci60,61,5244,8,40,21,grrf,select-HD,46.3,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,prostate,102,6033,2,67,35,grrf,select-HD,10.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,srbct,63,2308,4,41,22,grrf,select-HD,9.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,adenocarcinoma,76,9868,2,50,26,grrf-rf,select-HD,16.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,brain,42,5597,5,27,15,grrf-rf,select-HD,21.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.2.class,77,4869,2,50,27,grrf-rf,select-HD,34.6,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,breast.3.class,95,4869,3,62,33,grrf-rf,select-HD,39,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,colon,62,2000,2,40,22,grrf-rf,select-HD,17.1,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,leukemia,38,3051,2,25,13,grrf-rf,select-HD,8.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,lymphoma,62,4026,3,40,22,grrf-rf,select-HD,6.8,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,nci60,61,5244,8,40,21,grrf-rf,select-HD,38.2,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,prostate,102,6033,2,67,35,grrf-rf,select-HD,8.7,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Guided random forest in the RRF package,Deng,2013,arXiv preprint,srbct,63,2308,4,41,22,grrf-rf,select-HD,5,NA,default,1000,sqrt(p),100 runs train/test,paired t-test
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf,allround,26.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf,allround,12.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf,allround,28.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf,allround,17.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf,allround,4.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf,allround,1.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf,allround,11.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf,allround,3.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf,allround,5.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf,allround,14.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf,allround,24,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf,allround,23.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf,allround,15.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf,allround,16.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf,allround,3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf,allround,20.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf,allround,21.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf,allround,21.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf,allround,18.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf,allround,0.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf,allround,35.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf,allround,4.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf,allround,27.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf,allround,13.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf,allround,28.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf,allround,18.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf,allround,6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf,allround,2.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf,allround,14.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf,allround,3.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf,allround,6.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf,allround,17.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf,allround,24.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf,allround,1.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf,allround,25.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf,allround,17.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf,allround,17,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf,allround,3.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf,allround,37.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf,allround,39.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf,allround,86.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf,allround,18.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf,allround,37.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf,allround,0.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf,allround,5.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf,allround,30,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf,allround,12.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf,allround,29.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf,allround,19.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf,allround,6.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf,allround,3.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf,allround,10.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf,allround,4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf,allround,6.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf,allround,18.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf,allround,24,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf,allround,1.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf,allround,24.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf,allround,17.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf,allround,14.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf,allround,2.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf,allround,14.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf,allround,21,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf,allround,15.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf,allround,17.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf,allround,0.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf,allround,24.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf,allround,0.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf,allround,2.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf,allround,25.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf,allround,12.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf,allround,26.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf,allround,16.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf,allround,4.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf,allround,1.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf,allround,7.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf,allround,1.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf,allround,5.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf,allround,15.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf,allround,21.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf,allround,1.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf,allround,23.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf,allround,12,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf,allround,14.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf,allround,2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf,allround,14,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf,allround,20.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf,allround,12.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf,allround,16.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf,allround,0.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf,allround,21.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf,allround,0.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf,allround,0.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rotationForest,allround,6.9,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rotationForest,allround,1.43,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rotationForest,allround,26.15,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rotationForest,allround,18.73,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rotationForest,allround,1.05,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rotationForest,allround,1.6,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rotationForest,allround,6.67,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rotationForest,allround,2.3,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rotationForest,allround,4.3,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rotationForest,allround,9.62,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rotationForest,allround,22,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rotationForest,allround,1,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rotationForest,allround,2.3,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rotationForest,allround,17.6,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rotationForest,allround,7.66,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rotationForest,allround,1.5,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rotationForest,allround,13,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rotationForest,allround,28,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rotationForest,allround,14.56,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rotationForest,allround,18.72,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rotationForest,allround,28,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rotationForest,allround,30,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rotationForest,allround,3.35,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rotationForest,allround,1.64,NA,fixed/search,100,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rotationForest,allround,3.3,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rotationForest,allround,1,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rotationForest,allround,25.5,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rotationForest,allround,16.11,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rotationForest,allround,1.03,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rotationForest,allround,0.8,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rotationForest,allround,4.11,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rotationForest,allround,1.8,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rotationForest,allround,4,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rotationForest,allround,8.18,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rotationForest,allround,21.6,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rotationForest,allround,1,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rotationForest,allround,2,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rotationForest,allround,16.7,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rotationForest,allround,7.47,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rotationForest,allround,1.4,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rotationForest,allround,5.12,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rotationForest,allround,21,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rotationForest,allround,11,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rotationForest,allround,16.86,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rotationForest,allround,27,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rotationForest,allround,21.4,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rotationForest,allround,3,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rotationForest,allround,0,NA,fixed/search,ntree-tuning,NA,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-reliefF,allround,28.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-reliefF,allround,14,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-reliefF,allround,28.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-reliefF,allround,21,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-reliefF,allround,6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-reliefF,allround,0.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-reliefF,allround,7.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-reliefF,allround,4.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-reliefF,allround,6.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-reliefF,allround,17.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-reliefF,allround,23.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-reliefF,allround,1.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-reliefF,allround,25.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-reliefF,allround,18.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-reliefF,allround,15,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-reliefF,allround,3.25,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-reliefF,allround,14.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-reliefF,allround,21.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-reliefF,allround,15.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-reliefF,allround,18.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-reliefF,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-reliefF,allround,27.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-reliefF,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-reliefF,allround,2.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-reliefF,allround,23.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-reliefF,allround,12.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-reliefF,allround,25.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-reliefF,allround,18.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-reliefF,allround,4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-reliefF,allround,0.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-reliefF,allround,6.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-reliefF,allround,2.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-reliefF,allround,5.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-reliefF,allround,14.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-reliefF,allround,22.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-reliefF,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-reliefF,allround,23.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-reliefF,allround,13.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-reliefF,allround,14.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-reliefF,allround,2.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-reliefF,allround,14.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-reliefF,allround,21.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-reliefF,allround,12.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-reliefF,allround,17.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-reliefF,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-reliefF,allround,26.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-reliefF,allround,0.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-reliefF,allround,0.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-me,allround,28.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-me,allround,14,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-me,allround,28.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-me,allround,20.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-me,allround,6.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-me,allround,2.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-me,allround,11.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-me,allround,3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-me,allround,6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-me,allround,17.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-me,allround,24.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-me,allround,2.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-me,allround,23.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-me,allround,13,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-me,allround,15.07,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-me,allround,2.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-me,allround,14.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-me,allround,21.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-me,allround,14.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-me,allround,17.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-me,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-me,allround,20.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-me,allround,0.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-me,allround,2.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-me,allround,26.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-me,allround,12.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-me,allround,26.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-me,allround,17.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-me,allround,3.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-me,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-me,allround,7.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-me,allround,3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-me,allround,4.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-me,allround,14.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-me,allround,22.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-me,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-me,allround,22.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-me,allround,13,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-me,allround,14.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-me,allround,2.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-me,allround,14.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-me,allround,19.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-me,allround,11.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-me,allround,17.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-me,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-me,allround,20.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-me,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-me,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-me,allround,30,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-me,allround,12.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-me,allround,27.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-me,allround,20.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-me,allround,6.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-me,allround,2.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-me,allround,9.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-me,allround,3.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-me,allround,6.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-me,allround,17.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-me,allround,23,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-me,allround,1.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-me,allround,25.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-me,allround,18.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-me,allround,16.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-me,allround,2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-me,allround,14.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-me,allround,20,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-me,allround,15.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-me,allround,18.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-me,allround,0.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-me,allround,23.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-me,allround,0.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-me,allround,0.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-me,allround,24.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-me,allround,12.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-me,allround,25.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-me,allround,17.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-me,allround,4.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-me,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-me,allround,7.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-me,allround,3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-me,allround,5.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-me,allround,15.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-me,allround,22.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-me,allround,0.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-me,allround,22.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-me,allround,13.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-me,allround,14.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-me,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-me,allround,13.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-me,allround,20,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-me,allround,12.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-me,allround,16.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-me,allround,0.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-me,allround,21.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-me,allround,0.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-me,allround,0.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv1,allround,28.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv1,allround,14.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv1,allround,31.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv1,allround,21,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv1,allround,6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv1,allround,1.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv1,allround,10.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv1,allround,3.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv1,allround,6.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv1,allround,15.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv1,allround,24.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv1,allround,1.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv1,allround,24.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv1,allround,15.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv1,allround,15.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv1,allround,2.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv1,allround,14.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv1,allround,20.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv1,allround,14.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv1,allround,13.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv1,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv1,allround,20.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv1,allround,0.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv1,allround,1.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv1,allround,24.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv1,allround,12.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv1,allround,27.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv1,allround,16.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv1,allround,4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv1,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv1,allround,6.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv1,allround,3.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv1,allround,5.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv1,allround,12.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv1,allround,22.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv1,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv1,allround,22.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv1,allround,9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv1,allround,15,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv1,allround,1.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv1,allround,14.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv1,allround,19.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv1,allround,11.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv1,allround,12.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv1,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv1,allround,20.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv1,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv1,allround,0.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv2,allround,28,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv2,allround,18.82,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv2,allround,16.13,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv2,allround,14.81,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv2,allround,3.33,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv2,allround,10.56,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv2,allround,14.87,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv2,allround,9.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv2,allround,7.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv2,allround,12.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv2,allround,11.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv2,allround,20.48,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv2,allround,16.71,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv2,allround,12.1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv2,allround,17,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv2,allround,1.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv2,allround,13.12,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv2,allround,18.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv2,allround,7.75,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv2,allround,7.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv2,allround,26.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv2,allround,12.59,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv2,allround,2.99,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv2,allround,14.86,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv2,allround,27,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv2,allround,18.15,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv2,allround,15.03,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv2,allround,2.59,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv2,allround,2.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv2,allround,10.11,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv2,allround,12.63,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv2,allround,9.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv2,allround,7.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv2,allround,12.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv2,allround,11.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv2,allround,20.48,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv2,allround,16.71,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv2,allround,12.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv2,allround,16.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv2,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv2,allround,12.42,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv2,allround,16.93,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv2,allround,4.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv2,allround,8.44,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv2,allround,26.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv2,allround,12,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv2,allround,1.87,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv2,allround,14,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv3,allround,7.27,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv3,allround,6.18,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv3,allround,25.49,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv3,allround,7.04,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv3,allround,20,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv3,allround,1.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv3,allround,10.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv3,allround,1.06,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv3,allround,0.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv3,allround,1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv3,allround,11.43,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv3,allround,1.43,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv3,allround,10.83,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv3,allround,3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv3,allround,1.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv3,allround,1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv3,allround,10.08,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv3,allround,28.79,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv3,allround,0.65,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv3,allround,8.53,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv3,allround,26.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv3,allround,18.54,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv3,allround,0,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv3,allround,1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv3,allround,2.83,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv3,allround,3.53,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv3,allround,24.18,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv3,allround,2.22,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv3,allround,16.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv3,allround,1.11,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv3,allround,6.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv3,allround,1.06,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv3,allround,0.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv3,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv3,allround,11.43,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv3,allround,1.43,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv3,allround,10.83,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv3,allround,3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv3,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv3,allround,0.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv3,allround,9.36,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv3,allround,27.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv3,allround,0.65,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv3,allround,8.44,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv3,allround,26.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv3,allround,18,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv3,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv3,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv4,allround,29,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv4,allround,13.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv4,allround,31.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv4,allround,19.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv4,allround,6.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv4,allround,1.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv4,allround,14.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv4,allround,4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv4,allround,6.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv4,allround,15.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv4,allround,23.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv4,allround,1.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv4,allround,26.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv4,allround,14.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv4,allround,16,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv4,allround,2.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv4,allround,14.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv4,allround,20.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv4,allround,14.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv4,allround,18.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv4,allround,0.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv4,allround,37.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv4,allround,0.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv4,allround,5.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv4,allround,27.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv4,allround,12.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv4,allround,31.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv4,allround,18.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv4,allround,6.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv4,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv4,allround,9.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv4,allround,3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv4,allround,6.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv4,allround,18.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv4,allround,24.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv4,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv4,allround,25.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv4,allround,14.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv4,allround,15.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv4,allround,1.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv4,allround,14.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv4,allround,19.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv4,allround,11.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv4,allround,18.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv4,allround,0.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv4,allround,22.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv4,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv4,allround,5.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv5,allround,11.82,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv5,allround,16.76,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv5,allround,17.74,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv5,allround,5.56,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv5,allround,2.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv5,allround,0.56,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv5,allround,3.16,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv5,allround,12.46,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv5,allround,5.71,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv5,allround,11.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv5,allround,22.08,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv5,allround,1.43,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv5,allround,21.29,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv5,allround,13.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv5,allround,2.56,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv5,allround,1.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv5,allround,14.24,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv5,allround,31.11,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv5,allround,9.68,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv5,allround,15.19,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv5,allround,29.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv5,allround,37.75,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv5,allround,1.84,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv5,allround,16.83,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv5,allround,8.49,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv5,allround,13.99,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv5,allround,17.32,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv5,allround,2.96,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv5,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv5,allround,0.56,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv5,allround,3.16,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv5,allround,12.46,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv5,allround,5.71,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv5,allround,11.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv5,allround,22.08,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv5,allround,1.43,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv5,allround,21.29,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv5,allround,13.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv5,allround,2.56,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv5,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv5,allround,13.55,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv5,allround,18.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv5,allround,6.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv5,allround,15.11,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv5,allround,27.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv5,allround,37.34,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv5,allround,0.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv5,allround,16,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv6,allround,25.45,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv6,allround,15.29,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv6,allround,29.03,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv6,allround,19.26,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv6,allround,6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv6,allround,1.11,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv6,allround,10,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv6,allround,1.03,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv6,allround,6.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv6,allround,14.76,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv6,allround,23.64,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv6,allround,1.43,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv6,allround,23.65,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv6,allround,11.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv6,allround,14,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv6,allround,2.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv6,allround,14.68,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv6,allround,18.46,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv6,allround,10.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv6,allround,18.75,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv6,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv6,allround,36.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv6,allround,0.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv6,allround,3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-wv6,allround,23.64,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-wv6,allround,14,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-wv6,allround,26.13,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-wv6,allround,17.41,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-wv6,allround,5.33,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-wv6,allround,1.11,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-wv6,allround,6.32,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-wv6,allround,0.95,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-wv6,allround,5.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-wv6,allround,14.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-wv6,allround,22.08,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-wv6,allround,0.95,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-wv6,allround,21.29,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-wv6,allround,11.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-wv6,allround,13.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-wv6,allround,2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-wv6,allround,14.19,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-wv6,allround,15.38,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-wv6,allround,8.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-wv6,allround,18.75,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-wv6,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-wv6,allround,35.33,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-wv6,allround,0.47,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-wv6,allround,4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,sfs-rf,allround,21.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,sfs-rf,allround,11.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,sfs-rf,allround,26.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,sfs-rf,allround,16.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,sfs-rf,allround,3.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,sfs-rf,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,sfs-rf,allround,9.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,sfs-rf,allround,2.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,sfs-rf,allround,4.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,sfs-rf,allround,16.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,sfs-rf,allround,23.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,sfs-rf,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,sfs-rf,allround,22.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,sfs-rf,allround,12.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,sfs-rf,allround,14.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,sfs-rf,allround,1.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,sfs-rf,allround,24.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,sfs-rf,allround,21.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,sfs-rf,allround,10.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,sfs-rf,allround,16.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,sfs-rf,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,sfs-rf,allround,34.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,sfs-rf,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,sfs-rf,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,sbs-rf,allround,26.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,sbs-rf,allround,12.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,sbs-rf,allround,28.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,sbs-rf,allround,17.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,sbs-rf,allround,5.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,sbs-rf,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,sbs-rf,allround,11.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,sbs-rf,allround,3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,sbs-rf,allround,5.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,sbs-rf,allround,13,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,sbs-rf,allround,22.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,sbs-rf,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,sbs-rf,allround,22.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,sbs-rf,allround,15.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,sbs-rf,allround,14.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,sbs-rf,allround,1.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,sbs-rf,allround,32.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,sbs-rf,allround,26.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,sbs-rf,allround,12.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,sbs-rf,allround,17.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,sbs-rf,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,sbs-rf,allround,33.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,sbs-rf,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,sbs-rf,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,mod-sfs-rf,allround,20.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,mod-sfs-rf,allround,11.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,mod-sfs-rf,allround,26.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,mod-sfs-rf,allround,14.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,mod-sfs-rf,allround,3.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,mod-sfs-rf,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,mod-sfs-rf,allround,7.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,mod-sfs-rf,allround,2.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,mod-sfs-rf,allround,4.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,mod-sfs-rf,allround,12.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,mod-sfs-rf,allround,21.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,mod-sfs-rf,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,mod-sfs-rf,allround,23.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,mod-sfs-rf,allround,11.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,mod-sfs-rf,allround,13.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,mod-sfs-rf,allround,2.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,mod-sfs-rf,allround,14.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,mod-sfs-rf,allround,20.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,mod-sfs-rf,allround,10.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,mod-sfs-rf,allround,17.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,mod-sfs-rf,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,mod-sfs-rf,allround,32.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,mod-sfs-rf,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,mod-sfs-rf,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,mod-sbs-rf,allround,19.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,mod-sbs-rf,allround,11.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,mod-sbs-rf,allround,25.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,mod-sbs-rf,allround,15.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,mod-sbs-rf,allround,3.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,mod-sbs-rf,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,mod-sbs-rf,allround,7.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,mod-sbs-rf,allround,3.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,mod-sbs-rf,allround,5.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,mod-sbs-rf,allround,13,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,mod-sbs-rf,allround,21.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,mod-sbs-rf,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,mod-sbs-rf,allround,22.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,mod-sbs-rf,allround,13.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,mod-sbs-rf,allround,12.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,mod-sbs-rf,allround,1.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,mod-sbs-rf,allround,14.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,mod-sbs-rf,allround,20.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,mod-sbs-rf,allround,12.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,mod-sbs-rf,allround,17,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,mod-sbs-rf,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,mod-sbs-rf,allround,35.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,mod-sbs-rf,allround,0.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,mod-sbs-rf,allround,2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,optimal-rf,allround,19.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,optimal-rf,allround,11.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,optimal-rf,allround,25.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,optimal-rf,allround,13.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,optimal-rf,allround,3.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,optimal-rf,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,optimal-rf,allround,7.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,optimal-rf,allround,3.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,optimal-rf,allround,4.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,optimal-rf,allround,11,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,optimal-rf,allround,21.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,optimal-rf,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,optimal-rf,allround,22.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,optimal-rf,allround,11.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,optimal-rf,allround,12.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,optimal-rf,allround,1.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,optimal-rf,allround,20.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,optimal-rf,allround,20.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,optimal-rf,allround,12.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,optimal-rf,allround,16.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,optimal-rf,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,optimal-rf,allround,31.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,optimal-rf,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,optimal-rf,allround,0.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,clustering-rf,allround,22.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,clustering-rf,allround,12.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,clustering-rf,allround,25.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,clustering-rf,allround,17.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,clustering-rf,allround,2.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,clustering-rf,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,clustering-rf,allround,11.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,clustering-rf,allround,3.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,clustering-rf,allround,6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,clustering-rf,allround,12,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,clustering-rf,allround,23.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,clustering-rf,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,clustering-rf,allround,23.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,clustering-rf,allround,13.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,clustering-rf,allround,12.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,clustering-rf,allround,1.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,clustering-rf,allround,5.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,clustering-rf,allround,20.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,clustering-rf,allround,15.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,clustering-rf,allround,17.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,clustering-rf,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,clustering-rf,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,clustering-rf,allround,0.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,clustering-rf,allround,1.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-me-wv1,allround,30,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-me-wv1,allround,14.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-me-wv1,allround,29.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-me-wv1,allround,19.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-me-wv1,allround,6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-me-wv1,allround,2.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-me-wv1,allround,10.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-me-wv1,allround,3.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-me-wv1,allround,5.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-me-wv1,allround,17.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-me-wv1,allround,23.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-me-wv1,allround,2.8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-me-wv1,allround,23.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-me-wv1,allround,8,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-me-wv1,allround,14.92,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-me-wv1,allround,1.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-me-wv1,allround,14.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-me-wv1,allround,21.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-me-wv1,allround,14.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-me-wv1,allround,18.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-me-wv1,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-me-wv1,allround,22.4,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-me-wv1,allround,0.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-me-wv1,allround,1.7,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-me-wv1,allround,25.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-me-wv1,allround,13.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-me-wv1,allround,27.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-me-wv1,allround,17.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-me-wv1,allround,4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-me-wv1,allround,1.1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-me-wv1,allround,7.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-me-wv1,allround,2.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-me-wv1,allround,4.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-me-wv1,allround,13.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-me-wv1,allround,22.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-me-wv1,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-me-wv1,allround,21.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-me-wv1,allround,4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-me-wv1,allround,14.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-me-wv1,allround,1.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-me-wv1,allround,14.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-me-wv1,allround,19.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-me-wv1,allround,11.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-me-wv1,allround,17.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-me-wv1,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-me-wv1,allround,20.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-me-wv1,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-me-wv1,allround,0.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-me-wv3,allround,6.36,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-me-wv3,allround,3.53,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-me-wv3,allround,23.86,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-me-wv3,allround,1.12,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-me-wv3,allround,20,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-me-wv3,allround,1.11,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-me-wv3,allround,3.5,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-me-wv3,allround,1.6,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-me-wv3,allround,4.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-me-wv3,allround,0.96,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-me-wv3,allround,13.9,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-me-wv3,allround,0.48,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-me-wv3,allround,6.2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-me-wv3,allround,2,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-me-wv3,allround,1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-me-wv3,allround,1.3,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-me-wv3,allround,10.08,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-me-wv3,allround,28.79,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-me-wv3,allround,0.65,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-me-wv3,allround,8.54,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-me-wv3,allround,26.67,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-me-wv3,allround,18.55,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-me-wv3,allround,0,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-me-wv3,allround,1,NA,default,100,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rf-me-wv3,allround,2.83,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rf-me-wv3,allround,3.53,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rf-me-wv3,allround,23.86,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rf-me-wv3,allround,1.12,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rf-me-wv3,allround,20,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rf-me-wv3,allround,1.11,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rf-me-wv3,allround,3.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rf-me-wv3,allround,1.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rf-me-wv3,allround,4.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rf-me-wv3,allround,0.96,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rf-me-wv3,allround,13.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rf-me-wv3,allround,0.48,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rf-me-wv3,allround,6.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rf-me-wv3,allround,2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rf-me-wv3,allround,0.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rf-me-wv3,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rf-me-wv3,allround,10.08,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rf-me-wv3,allround,28.79,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rf-me-wv3,allround,0.65,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rf-me-wv3,allround,8.53,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rf-me-wv3,allround,26.67,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rf-me-wv3,allround,7.95,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rf-me-wv3,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rf-me-wv3,allround,1,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-wv1,allround,33.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-wv1,allround,14,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-wv1,allround,30.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-wv1,allround,20.6,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-wv1,allround,6.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-wv1,allround,3.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-wv1,allround,9.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-wv1,allround,3.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-wv1,allround,6.6,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-wv1,allround,18.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-wv1,allround,25,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-wv1,allround,1.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-wv1,allround,24.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-wv1,allround,13.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-wv1,allround,15.94,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-wv1,allround,2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-wv1,allround,14.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-wv1,allround,21,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-wv1,allround,16.6,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-wv1,allround,18.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-wv1,allround,0.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-wv1,allround,23.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-wv1,allround,0.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-wv1,allround,2.6,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-wv1,allround,23.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-wv1,allround,11.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-wv1,allround,26.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-wv1,allround,16.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-wv1,allround,4.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-wv1,allround,1.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-wv1,allround,6.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-wv1,allround,3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-wv1,allround,5.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-wv1,allround,15.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-wv1,allround,22.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-wv1,allround,0.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-wv1,allround,22.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-wv1,allround,9.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-wv1,allround,15.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-wv1,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-wv1,allround,14,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-wv1,allround,20.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-wv1,allround,11.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-wv1,allround,18.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-wv1,allround,0.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-wv1,allround,21,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-wv1,allround,0.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-wv1,allround,0.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-me-wv1,allround,32.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-me-wv1,allround,14.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-me-wv1,allround,29.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-me-wv1,allround,20.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-me-wv1,allround,6,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-me-wv1,allround,2.3,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-me-wv1,allround,8.8,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-me-wv1,allround,3.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-me-wv1,allround,5.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-me-wv1,allround,18.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-me-wv1,allround,24.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-me-wv1,allround,1.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-me-wv1,allround,25.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-me-wv1,allround,15.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-me-wv1,allround,15,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-me-wv1,allround,1.5,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-me-wv1,allround,14.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-me-wv1,allround,31.9,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-me-wv1,allround,14.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-me-wv1,allround,18.7,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-me-wv1,allround,0.4,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-me-wv1,allround,20.2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-me-wv1,allround,0.1,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-me-wv1,allround,2,NA,NA,100,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-me-wv1,allround,23.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-me-wv1,allround,13,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-me-wv1,allround,26.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-me-wv1,allround,17.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-me-wv1,allround,4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-me-wv1,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-me-wv1,allround,6.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-me-wv1,allround,3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-me-wv1,allround,5.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-me-wv1,allround,16.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-me-wv1,allround,22.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-me-wv1,allround,0.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-me-wv1,allround,22.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-me-wv1,allround,10.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-me-wv1,allround,14.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-me-wv1,allround,1.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-me-wv1,allround,13.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-me-wv1,allround,19.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-me-wv1,allround,12.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-me-wv1,allround,16.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-me-wv1,allround,0.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-me-wv1,allround,19.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-me-wv1,allround,0,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-me-wv1,allround,1.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,rk-rf-me-wv1,allround,23.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,rk-rf-me-wv1,allround,13,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,rk-rf-me-wv1,allround,26.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,rk-rf-me-wv1,allround,17.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,rk-rf-me-wv1,allround,4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,rk-rf-me-wv1,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,rk-rf-me-wv1,allround,6.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,rk-rf-me-wv1,allround,3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,rk-rf-me-wv1,allround,5.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,rk-rf-me-wv1,allround,16.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,rk-rf-me-wv1,allround,22.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,rk-rf-me-wv1,allround,0.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,rk-rf-me-wv1,allround,22.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,rk-rf-me-wv1,allround,10.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,rk-rf-me-wv1,allround,14.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,rk-rf-me-wv1,allround,1.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,rk-rf-me-wv1,allround,13.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,rk-rf-me-wv1,allround,19.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,rk-rf-me-wv1,allround,12.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,rk-rf-me-wv1,allround,16.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,rk-rf-me-wv1,allround,0.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,rk-rf-me-wv1,allround,19.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,rk-rf-me-wv1,allround,0,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,rk-rf-me-wv1,allround,1.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,optimal-rk-rf,allround,26.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,optimal-rk-rf,allround,10.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,optimal-rk-rf,allround,26.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,optimal-rk-rf,allround,14.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,optimal-rk-rf,allround,4.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,optimal-rk-rf,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,optimal-rk-rf,allround,7.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,optimal-rk-rf,allround,3.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,optimal-rk-rf,allround,5.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,optimal-rk-rf,allround,12.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,optimal-rk-rf,allround,19.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,optimal-rk-rf,allround,1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,optimal-rk-rf,allround,22.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,optimal-rk-rf,allround,10.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,optimal-rk-rf,allround,13.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,optimal-rk-rf,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,optimal-rk-rf,allround,19.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,optimal-rk-rf,allround,18,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,optimal-rk-rf,allround,12.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,optimal-rk-rf,allround,16.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,optimal-rk-rf,allround,0,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,optimal-rk-rf,allround,34.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,optimal-rk-rf,allround,0.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,optimal-rk-rf,allround,2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,optimal-rf-me,allround,21.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,optimal-rf-me,allround,11.9,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,optimal-rf-me,allround,26.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,optimal-rf-me,allround,17.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,optimal-rf-me,allround,4.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,optimal-rf-me,allround,1.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,optimal-rf-me,allround,8.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,optimal-rf-me,allround,2.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,optimal-rf-me,allround,4.6,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,optimal-rf-me,allround,13,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,optimal-rf-me,allround,21.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,optimal-rf-me,allround,1.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,optimal-rf-me,allround,23.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,optimal-rf-me,allround,7.4,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,optimal-rf-me,allround,12.7,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,optimal-rf-me,allround,1.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,optimal-rf-me,allround,13.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,optimal-rf-me,allround,26.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,optimal-rf-me,allround,10.2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,optimal-rf-me,allround,16.3,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,optimal-rf-me,allround,0,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,optimal-rf-me,allround,33.8,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,optimal-rf-me,allround,0.5,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,optimal-rf-me,allround,2,NA,default,ntree-tuning,sqrt(p),10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,breast,106,10,6,NA,NA,optimal-rk-rf-me,allround,20.6,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ecoli,336,8,8,NA,NA,optimal-rk-rf-me,allround,12.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,survival,306,3,2,NA,NA,optimal-rk-rf-me,allround,25.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,spectf heart,267,44,2,NA,NA,optimal-rk-rf-me,allround,17.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,iris,150,4,3,NA,NA,optimal-rk-rf-me,allround,4.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wine,178,13,3,NA,NA,optimal-rk-rf-me,allround,1.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,parkinsons,197,23,2,NA,NA,optimal-rk-rf-me,allround,7.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,wdbc,569,30,2,NA,NA,optimal-rk-rf-me,allround,3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ionosphere,351,34,2,NA,NA,optimal-rk-rf-me,allround,5.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,sonar,208,60,2,NA,NA,optimal-rk-rf-me,allround,13.9,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,pima,768,8,2,NA,NA,optimal-rk-rf-me,allround,20.8,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,glass,214,10,7,NA,NA,optimal-rk-rf-me,allround,0.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,vehicle,846,18,4,NA,NA,optimal-rk-rf-me,allround,22.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,alzheimers,108,14,2,NA,NA,optimal-rk-rf-me,allround,7.3,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,waveform,5000,40,3,NA,NA,optimal-rk-rf-me,allround,12.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,musk,6598,168,2,NA,NA,optimal-rk-rf-me,allround,1.4,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,balance,625,4,3,NA,NA,optimal-rk-rf-me,allround,13.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hays-roth,160,5,3,NA,NA,optimal-rk-rf-me,allround,15.1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,hepatitis,155,19,2,NA,NA,optimal-rk-rf-me,allround,10.2,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,mammo-mass,961,5,2,NA,NA,optimal-rk-rf-me,allround,16.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,post-opt,90,8,3,NA,NA,optimal-rk-rf-me,allround,0,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,ta-eval,151,5,3,NA,NA,optimal-rk-rf-me,allround,33.7,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,votes,435,16,2,NA,NA,optimal-rk-rf-me,allround,0.5,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Modifications of the construction and voting mechanisms of the random forests algorithm,Tripoliti et al.,2013,Data & Knowledge Engineering,zoo,101,16,7,NA,NA,optimal-rk-rf-me,allround,1,NA,NA,ntree-tuning,random,10-fold cv,omnibus + pos-hoc tests
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,audiology,226,69,24,NA,NA,rf,allround,30.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,balance,625,4,3,NA,NA,rf,allround,13.6,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer alt,286,10,2,NA,NA,rf,allround,26.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer,699,9,2,NA,NA,rf,allround,3.72,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,car,1728,6,4,NA,NA,rf,allround,3.53,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cleveland heart,307,13,5,NA,NA,rf,allround,43.23,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,credit,690,15,2,NA,NA,rf,allround,13.21,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cylinder,512,39,2,NA,NA,rf,allround,20.56,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,dermatology,366,33,6,NA,NA,rf,allround,1.91,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ecoli,336,7,8,NA,NA,rf,allround,12.8,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,glass,214,9,6,NA,NA,rf,allround,21.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,hepatitis,155,19,2,NA,NA,rf,allround,14.19,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,votes,435,16,2,NA,NA,rf,allround,3.91,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,segment,2310,19,7,NA,NA,rf,allround,7.62,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ionosphere,351,34,2,NA,NA,rf,allround,5.98,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,iris,150,4,3,NA,NA,rf,allround,5.33,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,kr-vs-kp,3196,36,2,NA,NA,rf,allround,1.31,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lung cancer,32,56,3,NA,NA,rf,allround,53.12,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lymphoma,62,4026,3,NA,NA,rf,allround,16.22,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,mushroom,8124,22,2,NA,NA,rf,allround,0,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,new-thyroid,7200,21,3,NA,NA,rf,allround,6.05,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,pima,768,8,2,NA,NA,rf,allround,23.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,post-opt,90,8,3,NA,NA,rf,allround,30,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tumor,339,17,22,NA,NA,rf,allround,53.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,promotor,106,57,2,NA,NA,rf,allround,19.81,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,sick-euthyroid,7200,21,2,NA,NA,rf,allround,2.12,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,soybean,683,35,19,NA,NA,rf,allround,9.77,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spam,4601,57,2,NA,NA,rf,allround,5.13,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spectf heart,267,44,2,NA,NA,rf,allround,9.74,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,splice,3190,60,3,NA,NA,rf,allround,3.64,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ta-eval,151,5,3,NA,NA,rf,allround,47.68,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tic-tac,958,9,2,NA,NA,rf,allround,2.3,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,wine,178,13,3,NA,NA,rf,allround,1.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,yeast,1484,8,10,NA,NA,rf,allround,37.94,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,audiology,226,69,24,NA,NA,rf-probVote,allround,24,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,balance,625,4,3,NA,NA,rf-probVote,allround,13.76,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer alt,286,10,2,NA,NA,rf-probVote,allround,27.27,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer,699,9,2,NA,NA,rf-probVote,allround,3.58,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,car,1728,6,4,NA,NA,rf-probVote,allround,3.01,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cleveland heart,307,13,5,NA,NA,rf-probVote,allround,43.23,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,credit,690,15,2,NA,NA,rf-probVote,allround,13.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cylinder,512,39,2,NA,NA,rf-probVote,allround,21.85,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,dermatology,366,33,6,NA,NA,rf-probVote,allround,1.91,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ecoli,336,7,8,NA,NA,rf-probVote,allround,12.2,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,glass,214,9,6,NA,NA,rf-probVote,allround,21.03,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,hepatitis,155,19,2,NA,NA,rf-probVote,allround,13.55,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,votes,435,16,2,NA,NA,rf-probVote,allround,3.45,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,segment,2310,19,7,NA,NA,rf-probVote,allround,6.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ionosphere,351,34,2,NA,NA,rf-probVote,allround,5.98,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,iris,150,4,3,NA,NA,rf-probVote,allround,4,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,kr-vs-kp,3196,36,2,NA,NA,rf-probVote,allround,1.13,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lung cancer,32,56,3,NA,NA,rf-probVote,allround,53.12,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lymphoma,62,4026,3,NA,NA,rf-probVote,allround,14.86,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,mushroom,8124,22,2,NA,NA,rf-probVote,allround,0,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,new-thyroid,7200,21,3,NA,NA,rf-probVote,allround,6.05,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,pima,768,8,2,NA,NA,rf-probVote,allround,23.18,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,post-opt,90,8,3,NA,NA,rf-probVote,allround,32.22,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tumor,339,17,22,NA,NA,rf-probVote,allround,53.1,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,promotor,106,57,2,NA,NA,rf-probVote,allround,12.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,sick-euthyroid,7200,21,2,NA,NA,rf-probVote,allround,2.09,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,soybean,683,35,19,NA,NA,rf-probVote,allround,9.77,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spam,4601,57,2,NA,NA,rf-probVote,allround,5.04,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spectf heart,267,44,2,NA,NA,rf-probVote,allround,8.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,splice,3190,60,3,NA,NA,rf-probVote,allround,3.51,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ta-eval,151,5,3,NA,NA,rf-probVote,allround,42.38,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tic-tac,958,9,2,NA,NA,rf-probVote,allround,1.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,wine,178,13,3,NA,NA,rf-probVote,allround,1.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,yeast,1484,8,10,NA,NA,rf-probVote,allround,37.87,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,audiology,226,69,24,NA,NA,rf-lp,allround,31.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,balance,625,4,3,NA,NA,rf-lp,allround,12.64,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer alt,286,10,2,NA,NA,rf-lp,allround,26.92,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer,699,9,2,NA,NA,rf-lp,allround,3.58,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,car,1728,6,4,NA,NA,rf-lp,allround,2.89,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cleveland heart,307,13,5,NA,NA,rf-lp,allround,42.9,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,credit,690,15,2,NA,NA,rf-lp,allround,13.35,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cylinder,512,39,2,NA,NA,rf-lp,allround,21.85,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,dermatology,366,33,6,NA,NA,rf-lp,allround,1.37,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ecoli,336,7,8,NA,NA,rf-lp,allround,12.2,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,glass,214,9,6,NA,NA,rf-lp,allround,23.36,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,hepatitis,155,19,2,NA,NA,rf-lp,allround,15.48,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,votes,435,16,2,NA,NA,rf-lp,allround,3.68,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,segment,2310,19,7,NA,NA,rf-lp,allround,7.62,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ionosphere,351,34,2,NA,NA,rf-lp,allround,5.98,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,iris,150,4,3,NA,NA,rf-lp,allround,4.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,kr-vs-kp,3196,36,2,NA,NA,rf-lp,allround,1.25,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lung cancer,32,56,3,NA,NA,rf-lp,allround,53.12,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lymphoma,62,4026,3,NA,NA,rf-lp,allround,15.54,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,mushroom,8124,22,2,NA,NA,rf-lp,allround,0,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,new-thyroid,7200,21,3,NA,NA,rf-lp,allround,6.05,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,pima,768,8,2,NA,NA,rf-lp,allround,23.31,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,post-opt,90,8,3,NA,NA,rf-lp,allround,32.22,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tumor,339,17,22,NA,NA,rf-lp,allround,53.1,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,promotor,106,57,2,NA,NA,rf-lp,allround,12.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,sick-euthyroid,7200,21,2,NA,NA,rf-lp,allround,2.28,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,soybean,683,35,19,NA,NA,rf-lp,allround,14.01,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spam,4601,57,2,NA,NA,rf-lp,allround,5.13,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spectf heart,267,44,2,NA,NA,rf-lp,allround,10.03,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,splice,3190,60,3,NA,NA,rf-lp,allround,3.61,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ta-eval,151,5,3,NA,NA,rf-lp,allround,45.7,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tic-tac,958,9,2,NA,NA,rf-lp,allround,1.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,wine,178,13,3,NA,NA,rf-lp,allround,1.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,yeast,1484,8,10,NA,NA,rf-lp,allround,37.94,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,audiology,226,69,24,NA,NA,rf-m1,allround,28.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,balance,625,4,3,NA,NA,rf-m1,allround,12.8,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer alt,286,10,2,NA,NA,rf-m1,allround,26.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer,699,9,2,NA,NA,rf-m1,allround,3.58,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,car,1728,6,4,NA,NA,rf-m1,allround,2.89,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cleveland heart,307,13,5,NA,NA,rf-m1,allround,42.24,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,credit,690,15,2,NA,NA,rf-m1,allround,13.35,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cylinder,512,39,2,NA,NA,rf-m1,allround,22.04,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,dermatology,366,33,6,NA,NA,rf-m1,allround,1.91,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ecoli,336,7,8,NA,NA,rf-m1,allround,12.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,glass,214,9,6,NA,NA,rf-m1,allround,22.43,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,hepatitis,155,19,2,NA,NA,rf-m1,allround,14.19,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,votes,435,16,2,NA,NA,rf-m1,allround,3.45,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,segment,2310,19,7,NA,NA,rf-m1,allround,6.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ionosphere,351,34,2,NA,NA,rf-m1,allround,5.7,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,iris,150,4,3,NA,NA,rf-m1,allround,4.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,kr-vs-kp,3196,36,2,NA,NA,rf-m1,allround,1.25,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lung cancer,32,56,3,NA,NA,rf-m1,allround,50,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lymphoma,62,4026,3,NA,NA,rf-m1,allround,16.22,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,mushroom,8124,22,2,NA,NA,rf-m1,allround,0,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,new-thyroid,7200,21,3,NA,NA,rf-m1,allround,6.51,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,pima,768,8,2,NA,NA,rf-m1,allround,23.44,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,post-opt,90,8,3,NA,NA,rf-m1,allround,30,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tumor,339,17,22,NA,NA,rf-m1,allround,52.21,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,promotor,106,57,2,NA,NA,rf-m1,allround,12.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,sick-euthyroid,7200,21,2,NA,NA,rf-m1,allround,2.4,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,soybean,683,35,19,NA,NA,rf-m1,allround,11.73,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spam,4601,57,2,NA,NA,rf-m1,allround,5.17,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spectf heart,267,44,2,NA,NA,rf-m1,allround,10.03,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,splice,3190,60,3,NA,NA,rf-m1,allround,3.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ta-eval,151,5,3,NA,NA,rf-m1,allround,43.71,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tic-tac,958,9,2,NA,NA,rf-m1,allround,1.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,wine,178,13,3,NA,NA,rf-m1,allround,1.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,yeast,1484,8,10,NA,NA,rf-m1,allround,38.07,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,audiology,226,69,24,NA,NA,rf-m2,allround,29.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,balance,625,4,3,NA,NA,rf-m2,allround,12.64,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer alt,286,10,2,NA,NA,rf-m2,allround,26.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer,699,9,2,NA,NA,rf-m2,allround,3.58,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,car,1728,6,4,NA,NA,rf-m2,allround,3.07,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cleveland heart,307,13,5,NA,NA,rf-m2,allround,44.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,credit,690,15,2,NA,NA,rf-m2,allround,13.35,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cylinder,512,39,2,NA,NA,rf-m2,allround,23.7,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,dermatology,366,33,6,NA,NA,rf-m2,allround,1.91,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ecoli,336,7,8,NA,NA,rf-m2,allround,13.1,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,glass,214,9,6,NA,NA,rf-m2,allround,22.9,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,hepatitis,155,19,2,NA,NA,rf-m2,allround,16.77,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,votes,435,16,2,NA,NA,rf-m2,allround,3.68,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,segment,2310,19,7,NA,NA,rf-m2,allround,6.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ionosphere,351,34,2,NA,NA,rf-m2,allround,6.27,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,iris,150,4,3,NA,NA,rf-m2,allround,4.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,kr-vs-kp,3196,36,2,NA,NA,rf-m2,allround,1.31,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lung cancer,32,56,3,NA,NA,rf-m2,allround,50,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lymphoma,62,4026,3,NA,NA,rf-m2,allround,17.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,mushroom,8124,22,2,NA,NA,rf-m2,allround,0,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,new-thyroid,7200,21,3,NA,NA,rf-m2,allround,5.12,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,pima,768,8,2,NA,NA,rf-m2,allround,23.18,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,post-opt,90,8,3,NA,NA,rf-m2,allround,28.89,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tumor,339,17,22,NA,NA,rf-m2,allround,53.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,promotor,106,57,2,NA,NA,rf-m2,allround,12.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,sick-euthyroid,7200,21,2,NA,NA,rf-m2,allround,2.56,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,soybean,683,35,19,NA,NA,rf-m2,allround,13.68,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spam,4601,57,2,NA,NA,rf-m2,allround,5.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spectf heart,267,44,2,NA,NA,rf-m2,allround,11.17,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,splice,3190,60,3,NA,NA,rf-m2,allround,3.54,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ta-eval,151,5,3,NA,NA,rf-m2,allround,45.7,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tic-tac,958,9,2,NA,NA,rf-m2,allround,1.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,wine,178,13,3,NA,NA,rf-m2,allround,1.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,yeast,1484,8,10,NA,NA,rf-m2,allround,38.01,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,audiology,226,69,24,NA,NA,rf-mk,allround,50.5,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,balance,625,4,3,NA,NA,rf-mk,allround,12.48,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer alt,286,10,2,NA,NA,rf-mk,allround,26.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,breast cancer,699,9,2,NA,NA,rf-mk,allround,3.58,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,car,1728,6,4,NA,NA,rf-mk,allround,4.75,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cleveland heart,307,13,5,NA,NA,rf-mk,allround,45.87,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,credit,690,15,2,NA,NA,rf-mk,allround,13.35,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,cylinder,512,39,2,NA,NA,rf-mk,allround,23.7,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,dermatology,366,33,6,NA,NA,rf-mk,allround,2.46,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ecoli,336,7,8,NA,NA,rf-mk,allround,14.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,glass,214,9,6,NA,NA,rf-mk,allround,26.64,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,hepatitis,155,19,2,NA,NA,rf-mk,allround,16.77,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,votes,435,16,2,NA,NA,rf-mk,allround,3.68,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,segment,2310,19,7,NA,NA,rf-mk,allround,7.62,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ionosphere,351,34,2,NA,NA,rf-mk,allround,6.27,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,iris,150,4,3,NA,NA,rf-mk,allround,4.67,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,kr-vs-kp,3196,36,2,NA,NA,rf-mk,allround,1.31,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lung cancer,32,56,3,NA,NA,rf-mk,allround,50,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,lymphoma,62,4026,3,NA,NA,rf-mk,allround,17.57,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,mushroom,8124,22,2,NA,NA,rf-mk,allround,0,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,new-thyroid,7200,21,3,NA,NA,rf-mk,allround,5.12,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,pima,768,8,2,NA,NA,rf-mk,allround,23.18,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,post-opt,90,8,3,NA,NA,rf-mk,allround,28.89,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tumor,339,17,22,NA,NA,rf-mk,allround,61.65,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,promotor,106,57,2,NA,NA,rf-mk,allround,12.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,sick-euthyroid,7200,21,2,NA,NA,rf-mk,allround,2.56,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,soybean,683,35,19,NA,NA,rf-mk,allround,18.89,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spam,4601,57,2,NA,NA,rf-mk,allround,5.26,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,spectf heart,267,44,2,NA,NA,rf-mk,allround,11.17,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,splice,3190,60,3,NA,NA,rf-mk,allround,3.51,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,ta-eval,151,5,3,NA,NA,rf-mk,allround,44.37,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,tic-tac,958,9,2,NA,NA,rf-mk,allround,1.88,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,wine,178,13,3,NA,NA,rf-mk,allround,1.69,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
Estimating class probabilities in random forests,Bostrom,2007,Machine Learning and Applications,yeast,1484,8,10,NA,NA,rf-mk,allround,41.58,NA,default,50,sqrt(p),10-fold cv,double-sided binomial tail probability
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene breast cancer,97,24481,2,87,10,tree-c4.5,HD,37.1,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene lung cancer,181,12533,2,162,19,tree-c4.5,HD,5,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,lymphoma,47,4026,2,42,5,tree-c4.5,HD,21.3,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene leukemia,72,7129,2,64,8,tree-c4.5,HD,20.8,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,colon,62,2000,2,55,7,tree-c4.5,HD,17.7,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,ovarian,253,15154,2,227,26,tree-c4.5,HD,4.3,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene prostate,21,12600,2,18,3,tree-c4.5,HD,66.7,NA,default,1,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene breast cancer,97,24481,2,87,10,rf,HD,38.1,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene lung cancer,181,12533,2,162,19,rf,HD,1.7,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,lymphoma,47,4026,2,42,5,rf,HD,19.1,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene leukemia,72,7129,2,64,8,rf,HD,13.9,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,colon,62,2000,2,55,7,rf,HD,24.2,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,ovarian,253,15154,2,227,26,rf,HD,5.9,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene prostate,21,12600,2,18,3,rf,HD,47.6,NA,default,25,sqrt(p),10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene breast cancer,97,24481,2,87,10,adaboost,HD,38.1,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene lung cancer,181,12533,2,162,19,adaboost,HD,3.9,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,lymphoma,47,4026,2,42,5,adaboost,HD,14.9,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene leukemia,72,7129,2,64,8,adaboost,HD,12.5,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,colon,62,2000,2,55,7,adaboost,HD,22.6,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,ovarian,253,15154,2,227,26,adaboost,HD,4.3,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene prostate,21,12600,2,18,3,adaboost,HD,66.7,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene breast cancer,97,24481,2,87,10,bagging,HD,34,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene lung cancer,181,12533,2,162,19,bagging,HD,2.8,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,lymphoma,47,4026,2,42,5,bagging,HD,14.9,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene leukemia,72,7129,2,64,8,bagging,HD,13.9,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,colon,62,2000,2,55,7,bagging,HD,17.7,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,ovarian,253,15154,2,227,26,bagging,HD,2.4,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene prostate,21,12600,2,18,3,bagging,HD,57.1,NA,none,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene breast cancer,97,24481,2,87,10,cs4,HD,32,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene lung cancer,181,12533,2,162,19,cs4,HD,1.1,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,lymphoma,47,4026,2,42,5,cs4,HD,8.5,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene leukemia,72,7129,2,64,8,cs4,HD,1.4,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,colon,62,2000,2,55,7,cs4,HD,17.7,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,ovarian,253,15154,2,227,26,cs4,HD,0.8,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene prostate,21,12600,2,18,3,cs4,HD,52.4,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene breast cancer,97,24481,2,87,10,mdmt,HD,35.7,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene lung cancer,181,12533,2,162,19,mdmt,HD,1.1,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,lymphoma,47,4026,2,42,5,mdmt,HD,5.9,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene leukemia,72,7129,2,64,8,mdmt,HD,2.5,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,colon,62,2000,2,55,7,mdmt,HD,14.2,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,ovarian,253,15154,2,227,26,mdmt,HD,3.6,NA,default,25,NA,10-fold cv,simple diff
A maximally diversified multiple decision tree algorithm for microarray data classification,Hu et al.,2006,Intelligent Systems for Bioinformatics,gene prostate,21,12600,2,18,3,mdmt,HD,40,NA,default,25,NA,10-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,22.6,3.8,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,8.9,3.2,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,42,3.8,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,10.4,4,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,19.9,2.5,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,8.1,1.1,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,40.1,2.9,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,8.8,1.5,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,19.1,2.4,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,8,2.3,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,38.9,3.1,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,8.1,1.9,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,18.8,2.1,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,8.3,1.9,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,38.8,3.9,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,8.2,1.5,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,19.2,1.9,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,7.9,1,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,40.8,1.7,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,8,0.6,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,24.4,2.6,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,11.8,2.5,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,42.4,4.8,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,14.2,2.6,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,23.2,3.9,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,12.3,2.9,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,40.9,3.3,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,10.6,2.8,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,21.2,2.6,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,12,2.1,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,39.9,2.5,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,9.5,1.8,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,22.6,2.2,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,11.8,2.4,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,40.8,2.8,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,9.6,2,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,18.6,2.1,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,8.1,1,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,41.5,1.7,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,8.7,1.8,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,13.1,1.4,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.6,0.5,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,21.9,3.6,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,3.5,2.6,fixed,20,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.8,1.5,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,22.4,2.2,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.8,1.6,fixed,50,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.5,0.7,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,21.8,1.9,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.9,0.9,fixed,80,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.8,1.4,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,21,2.6,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.4,0.7,fixed,100,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,11.8,1,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.9,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,20.5,1.8,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.5,0.7,fixed,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,19.2,1.9,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,6.9,1,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,40.8,1.7,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,8,0.6,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,19.2,2.1,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,6.5,1.9,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,39.6,2.3,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,7.6,1.4,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,18,1.6,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,6.9,1.4,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,39.9,2.3,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,7.6,1.2,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,18.8,1.5,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,6.4,1.6,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,39.7,2.2,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,7.8,1.6,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,18.2,1.3,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,6.3,1.9,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,39.8,1.8,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,7.7,1,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,18.6,2.1,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,8.1,1,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,41.5,1.7,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,8.7,1.8,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,22,2,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,10.8,1.2,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,38.5,1.8,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,8.2,1.6,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,22.8,1.9,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,10.5,1.4,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,38.7,2,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,9.3,1.3,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,21.2,2.9,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,10.9,1.7,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,39.1,0.9,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,8.3,1.4,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,erf,HD,22,2.5,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,erf,HD,11,1.6,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,erf,HD,38.9,1.5,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,erf,HD,8.7,1.6,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,11.8,1,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.9,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,20.5,1.8,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.5,0.7,nmin=1,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.5,1.3,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,21.3,2.1,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.2,0.5,nmin=2,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.2,1.1,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,20.6,1.5,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.6,0.5,nmin=4,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.6,1.4,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,20.9,2.1,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.6,0.7,nmin=6,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf,HD,12.6,1.4,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf,HD,0.5,0.4,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf,HD,20.5,1.8,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf,HD,2.7,0.4,nmin=10,200,50*log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,20.8,11.5,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,12.8,8.6,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,44.4,13.9,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,12.7,2.5,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,20.6,15.8,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,12.8,8.6,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,39.7,8.6,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,7.9,4.5,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,17.7,6.6,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,5.3,5.6,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,36.2,10.2,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,6.9,4.4,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,17.7,6.6,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,7.8,5.4,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,39.8,5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,5.9,4.1,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,rf,HD,17.7,6.6,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,rf,HD,7.9,8.6,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,rf,HD,44.7,7.1,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,rf,HD,6.9,5.6,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(chi),HD,19.2,6.9,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(chi),HD,7.8,5.4,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(chi),HD,39.7,12,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(chi),HD,7.9,2.7,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(chi),HD,17.7,6.6,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(chi),HD,7.8,2.7,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(chi),HD,34.8,9,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(chi),HD,6.9,2.7,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(chi),HD,21.2,5.1,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(chi),HD,10.4,7.4,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(chi),HD,34.7,8,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(chi),HD,8.9,9.6,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(chi),HD,21.2,5.1,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(chi),HD,10.4,7.4,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(chi),HD,38.7,8,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(chi),HD,7.9,6.9,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(chi),HD,21.2,5.1,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(chi),HD,10.4,7.4,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(chi),HD,38.7,8,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(chi),HD,7.9,6.9,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(lda),HD,17.7,6.6,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(lda),HD,6,3.7,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(lda),HD,39.7,9.3,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(lda),HD,5.9,4.1,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(lda),HD,17.7,6.6,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(lda),HD,6,3.7,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(lda),HD,34.8,14.8,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(lda),HD,6.9,2.7,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(lda),HD,17.7,6.6,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(lda),HD,6.7,8.2,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(lda),HD,34.8,8.7,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(lda),HD,8.9,9.6,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(lda),HD,16.2,5.9,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(lda),HD,6.7,8.2,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(lda),HD,38.2,5.6,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(lda),HD,7.9,7.5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,enrichRF(lda),HD,16,6.6,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,enrichRF(lda),HD,6.7,8.2,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,enrichRF(lda),HD,43,8.7,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,enrichRF(lda),HD,7.9,7.5,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,orf-rnd,HD,17.7,6.6,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,orf-rnd,HD,6.9,4.4,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,orf-rnd,HD,37.4,13.5,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,orf-rnd,HD,9.9,7.1,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,orf-rnd,HD,11.5,7.3,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,orf-rnd,HD,2.9,3.5,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,orf-rnd,HD,32.5,11.2,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,orf-rnd,HD,4.9,4,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,orf-rnd,HD,12.9,9.5,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,orf-rnd,HD,2.9,3.5,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,orf-rnd,HD,34.8,7.6,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,orf-rnd,HD,3.9,4.3,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,orf-rnd,HD,14.5,10.5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,orf-rnd,HD,6.7,8.2,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,orf-rnd,HD,36.5,12.8,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,orf-rnd,HD,3.9,4,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,orf-rnd,HD,16,12.5,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,orf-rnd,HD,6.7,8.6,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,orf-rnd,HD,38.2,11.4,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,orf-rnd,HD,5.9,6.5,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(chi),HD,17.6,8.6,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(chi),HD,6.5,4.4,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(chi),HD,36.7,8.6,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(chi),HD,7.4,2.7,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(chi),HD,11.9,9.5,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(chi),HD,1.3,3,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(chi),HD,31.8,7.6,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(chi),HD,4.4,4.9,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(chi),HD,12.9,9.5,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(chi),HD,2.6,3.5,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(chi),HD,31.7,11.8,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(chi),HD,2.7,4.3,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(chi),HD,12.9,9.5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(chi),HD,2.6,3.5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(chi),HD,36.5,14.1,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(chi),HD,3.9,4,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(chi),HD,16,12.5,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(chi),HD,1.3,3,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(chi),HD,38.2,11.4,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(chi),HD,5.9,6.5,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(lda),HD,16.8,7.5,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(lda),HD,5.3,3,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(lda),HD,30,11.7,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(lda),HD,6.2,2.7,default,200,log(p+1),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(lda),HD,11.3,7.3,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(lda),HD,2.6,3.5,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(lda),HD,26.8,11.5,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(lda),HD,2.5,4.4,default,200,sqrt(p),5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(lda),HD,11.3,7.3,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(lda),HD,1.3,2.8,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(lda),HD,20.6,8.1,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(lda),HD,2.5,2.7,fixed,200,p/10,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(lda),HD,12.9,9.5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(lda),HD,2.6,3.5,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(lda),HD,38.2,11.4,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(lda),HD,3.9,4,fixed,200,p/4,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,colon,62,2000,2,49,13,srf(lda),HD,13.6,6.6,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,dlbcl,77,5469,2,61,16,srf(lda),HD,1.3,3,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,embryonal-t,60,7129,2,48,12,srf(lda),HD,38.2,11.4,fixed,200,p/2,5-fold cv,simple diff
Stratified sampling for feature subspace selection in random forests for high dimensional data,Ye et al.,2013,Pattern Recognition,prostate-t,102,10509,2,81,21,srf(lda),HD,5.9,6.5,fixed,200,p/2,5-fold cv,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),gamma,19020,10,2,12553,6467,sfs-rf,allround,11.07,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),letters,20000,16,26,13200,6800,sfs-rf,allround,3.07,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),pendigits,10992,16,10,7254,3738,sfs-rf,allround,0.41,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),segment,2310,19,7,1524,786,sfs-rf,allround,0.66,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),spam,4610,57,2,3042,1568,sfs-rf,allround,3.33,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),vehicle,946,18,4,624,322,sfs-rf,allround,14.29,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),waveform,5000,40,3,3300,1700,sfs-rf,allround,10.16,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),ringnorm,7400,20,2,4884,2516,sfs-rf,allround,1.9,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),twonorm,7400,20,2,4884,2516,sfs-rf,allround,1.82,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),mnist,60000,84,10,39600,20400,sfs-rf,allround,4.41,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),gamma,19020,10,2,12553,6467,sbs-rf,allround,11.17,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),letters,20000,16,26,13200,6800,sbs-rf,allround,3.2,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),pendigits,10992,16,10,7254,3738,sbs-rf,allround,0.57,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),segment,2310,19,7,1524,786,sbs-rf,allround,1.57,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),spam,4610,57,2,3042,1568,sbs-rf,allround,3.98,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),vehicle,946,18,4,624,322,sbs-rf,allround,19.64,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),waveform,5000,40,3,3300,1700,sbs-rf,allround,10.46,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),ringnorm,7400,20,2,4884,2516,sbs-rf,allround,2.15,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),twonorm,7400,20,2,4884,2516,sbs-rf,allround,2.19,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),mnist,60000,84,10,39600,20400,sbs-rf,allround,4.4,NA,default,ntree-tuning,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),gamma,19020,10,2,12553,6467,rf,allround,12.19,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),letters,20000,16,26,13200,6800,rf,allround,4.09,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),pendigits,10992,16,10,7254,3738,rf,allround,1.01,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),segment,2310,19,7,1524,786,rf,allround,2.49,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),spam,4610,57,2,3042,1568,rf,allround,5.22,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),vehicle,946,18,4,624,322,rf,allround,26.79,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),waveform,5000,40,3,3300,1700,rf,allround,14,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),ringnorm,7400,20,2,4884,2516,rf,allround,3.33,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),twonorm,7400,20,2,4884,2516,rf,allround,3.2,NA,default,300,sqrt(p),1 run train/test,simple diff
On the selection of decision trees in random forests,Bernard et al.,2008,Neural Networks (IJCNN),mnist,60000,84,10,39600,20400,rf,allround,4.93,NA,default,300,sqrt(p),1 run train/test,simple diff
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,anneal,898,38,5,592,306,tree-cart,allround,9.8,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,aus credit,690,14,2,455,235,tree-cart,allround,14.5,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,auto,205,25,6,135,70,tree-cart,allround,37.6,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,breast cancer,699,9,2,461,238,tree-cart,allround,5.8,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,credit,690,15,2,455,235,tree-cart,allround,14.8,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,german credit,1000,24,2,660,340,tree-cart,allround,27.4,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,glass,214,9,6,141,73,tree-cart,allround,34.2,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,heart,270,13,2,178,92,tree-cart,allround,21.9,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,hepatitis,155,19,2,102,53,tree-cart,allround,20.9,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,horse,368,22,2,242,126,tree-cart,allround,16.4,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,iris,150,4,3,99,51,tree-cart,allround,6.4,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,labor,57,16,2,37,20,tree-cart,allround,22.3,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,led7,3200,7,10,2112,1088,tree-cart,allround,31.8,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,lymphoma,148,18,3,97,51,tree-cart,allround,26.8,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,pima,768,8,2,506,262,tree-cart,allround,26,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,tic-tac,958,9,2,632,326,tree-cart,allround,9.4,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,vehicle,846,18,4,558,288,tree-cart,allround,32.5,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,waveform,3300,21,3,2178,1122,tree-cart,allround,26.2,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,wine,178,13,3,117,61,tree-cart,allround,12.2,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,zoo,101,16,7,66,35,tree-cart,allround,21.1,NA,NA,1,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,anneal,898,38,5,592,306,inTreeStel,interpret,7,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,aus credit,690,14,2,455,235,inTreeStel,interpret,15.7,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,auto,205,25,6,135,70,inTreeStel,interpret,26.2,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,breast cancer,699,9,2,461,238,inTreeStel,interpret,4.8,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,credit,690,15,2,455,235,inTreeStel,interpret,15.9,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,german credit,1000,24,2,660,340,inTreeStel,interpret,28.6,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,glass,214,9,6,141,73,inTreeStel,interpret,31,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,heart,270,13,2,178,92,inTreeStel,interpret,22.4,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,hepatitis,155,19,2,102,53,inTreeStel,interpret,21.1,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,horse,368,22,2,242,126,inTreeStel,interpret,19.7,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,iris,150,4,3,99,51,inTreeStel,interpret,4.7,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,labor,57,16,2,37,20,inTreeStel,interpret,14.8,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,led7,3200,7,10,2112,1088,inTreeStel,interpret,26.9,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,lymphoma,148,18,3,97,51,inTreeStel,interpret,20.9,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,pima,768,8,2,506,262,inTreeStel,interpret,27.2,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,tic-tac,958,9,2,632,326,inTreeStel,interpret,0.2,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,vehicle,846,18,4,558,288,inTreeStel,interpret,28.5,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,waveform,3300,21,3,2178,1122,inTreeStel,interpret,19.8,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,wine,178,13,3,117,61,inTreeStel,interpret,8.6,NA,NA,100,NA,100 runs train/test,paired t-test
Interpreting tree ensembles with inTrees,Deng,2014,arXiv preprint,zoo,101,16,7,66,35,inTreeStel,interpret,6.1,NA,NA,100,NA,100 runs train/test,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,afp,9974,19,2,8976,998,rf,allround,22.95,0.65,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,balance,625,4,3,562,63,rf,allround,15.53,3.96,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,biodeg,1055,41,2,949,106,rf,allround,13.08,3,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer alt,699,9,2,629,70,rf,allround,3.16,1.94,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer,106,9,6,95,11,rf,allround,30.15,12.64,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,car,1728,6,4,1555,173,rf,allround,3.18,1.39,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,climate,540,18,2,486,54,rf,allround,7.47,3.42,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ecoli,336,7,8,302,34,rf,allround,13.96,6.1,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hepatitis,155,19,2,139,16,rf,allround,15.61,9.25,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley noise,1212,100,2,606,606,rf,allround,44.84,1.1,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley no noise,1212,100,2,606,606,rf,allround,44.33,0.74,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,iris,150,4,3,135,15,rf,allround,5.11,5.14,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,letters,"20,000",16,26,15000,"5,000",rf,allround,3.88,0.17,default,100,sqrt(p),15 runs 75/25,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,movement,360,90,25,324,36,rf,allround,18.43,6.38,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,lymphoma,298,18,4,268,30,rf,allround,1.27,2.88,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,musk,467,167,2,420,47,rf,allround,0.33,0.87,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,nursery,"12,960",8,5,11664,"1,296",rf,allround,0.37,0.2,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,orl,400,1024,40,200,200,rf,allround,10.75,1.79,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ozone,2536,72,2,2282,254,rf,allround,3.01,1.11,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,page blocks,5473,10,5,4925,548,rf,allround,2.53,0.66,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,parkinsons,195,22,2,175,20,rf,allround,9.22,6.39,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,pie,"11,560",1024,68,2040,"9,520",rf,allround,22.72,0.63,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,planning relax,182,12,2,163,19,rf,allround,29.56,9.82,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,polyandenylation,9255,169,2,4418,4837,rf,allround,35.61,0.34,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,seeds,210,7,3,189,21,rf,allround,6.63,5.21,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,smpred,"12,563",100,2,2614,"9,949",rf,allround,4.6,0.11,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,soybean,683,35,19,307,376,rf,allround,5.46,4.36,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,spect heart,267,22,2,80,187,rf,allround,25.39,1.74,default,100,sqrt(p),15 runs 33/66,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,splice,3190,59,2,2871,319,rf,allround,5.01,1.12,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,steel plates faults,1941,27,7,1746,195,rf,allround,21.7,2.93,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,heart,270,13,2,243,27,rf,allround,17.59,6.73,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vehicle,846,18,4,761,85,rf,allround,25.31,4.32,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 2c,310,6,2,279,31,rf,allround,15.96,7.2,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 3c,310,6,3,279,31,rf,allround,15.23,5.95,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform1,5000,21,3,4500,500,rf,allround,15.06,1.67,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform2,5000,40,3,4500,500,rf,allround,15.09,1.52,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine,178,13,3,160,18,rf,allround,2.22,3.68,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality red,1599,11,6,1439,160,rf,allround,29.32,3.71,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality white,4898,11,7,4408,490,rf,allround,30.12,2.17,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yeast,1484,8,10,1335,149,rf,allround,38.42,3.81,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yale,165,1024,15,75,90,rf,allround,29.07,2.87,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yaleB,2432,1024,38,1216,1216,rf,allround,3.13,0.44,default,100,sqrt(p),15 runs 40/60,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,afp,9974,19,2,8976,998,pca-rf,allround,26.35,0.69,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,balance,625,4,3,562,63,pca-rf,allround,12.41,3.53,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,biodeg,1055,41,2,949,106,pca-rf,allround,12.71,3.01,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer alt,699,9,2,629,70,pca-rf,allround,2.92,2.01,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer,106,9,6,95,11,pca-rf,allround,26.8,13.39,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,car,1728,6,4,1555,173,pca-rf,allround,2.95,1.51,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,climate,540,18,2,486,54,pca-rf,allround,8.81,3.38,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ecoli,336,7,8,302,34,pca-rf,allround,13,5.52,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hepatitis,155,19,2,139,16,pca-rf,allround,15.6,8.77,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley noise,1212,100,2,606,606,pca-rf,allround,10.44,1.22,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley no noise,1212,100,2,606,606,pca-rf,allround,0.18,0.42,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,iris,150,4,3,135,15,pca-rf,allround,4.53,4.48,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,letters,"20,000",16,26,15000,"5,000",pca-rf,allround,3.67,0.1,default,100,sqrt(p),15 runs 75/25,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,movement,360,90,25,324,36,pca-rf,allround,12.24,5.57,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,lymphoma,298,18,4,268,30,pca-rf,allround,0.97,2.5,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,musk,467,167,2,420,47,pca-rf,allround,0.38,0.88,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,nursery,"12,960",8,5,11664,"1,296",pca-rf,allround,0.29,0.15,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,orl,400,1024,40,200,200,pca-rf,allround,25,2.94,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ozone,2536,72,2,2282,254,pca-rf,allround,2.89,1.13,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,page blocks,5473,10,5,4925,548,pca-rf,allround,2.45,0.62,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,parkinsons,195,22,2,175,20,pca-rf,allround,8.23,6.15,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,pie,"11,560",1024,68,2040,"9,520",pca-rf,allround,19.81,0.65,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,planning relax,182,12,2,163,19,pca-rf,allround,29.37,9.38,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,polyandenylation,9255,169,2,4418,4837,pca-rf,allround,38.6,0.72,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,seeds,210,7,3,189,21,pca-rf,allround,6.13,5.21,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,smpred,"12,563",100,2,2614,"9,949",pca-rf,allround,4.13,0.15,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,soybean,683,35,19,307,376,pca-rf,allround,4.97,4.79,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,spect heart,267,22,2,80,187,pca-rf,allround,22.59,1.22,default,100,sqrt(p),15 runs 33/66,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,splice,3190,59,2,2871,319,pca-rf,allround,9.04,1.62,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,steel plates faults,1941,27,7,1746,195,pca-rf,allround,22.12,3.11,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,heart,270,13,2,243,27,pca-rf,allround,16.54,6.77,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vehicle,846,18,4,761,85,pca-rf,allround,21.33,4.47,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 2c,310,6,2,279,31,pca-rf,allround,14.67,7,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 3c,310,6,3,279,31,pca-rf,allround,14.26,5.76,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform1,5000,21,3,4500,500,pca-rf,allround,13.81,1.53,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform2,5000,40,3,4500,500,pca-rf,allround,13.72,1.62,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine,178,13,3,160,18,pca-rf,allround,2.68,3.82,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality red,1599,11,6,1439,160,pca-rf,allround,29.03,3.66,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality white,4898,11,7,4408,490,pca-rf,allround,29.86,1.91,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yeast,1484,8,10,1335,149,pca-rf,allround,37.82,3.65,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yale,165,1024,15,75,90,pca-rf,allround,37.6,4.48,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yaleB,2432,1024,38,1216,1216,pca-rf,allround,19.03,1.11,default,100,sqrt(p),15 runs 40/60,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,afp,9974,19,2,8976,998,lda-rf,allround,16.26,0.71,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,balance,625,4,3,562,63,lda-rf,allround,11.46,3.44,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,biodeg,1055,41,2,949,106,lda-rf,allround,12.55,3.07,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer alt,699,9,2,629,70,lda-rf,allround,3.02,1.91,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer,106,9,6,95,11,lda-rf,allround,27.64,13.01,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,car,1728,6,4,1555,173,lda-rf,allround,2.8,1.41,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,climate,540,18,2,486,54,lda-rf,allround,7.7,3.5,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ecoli,336,7,8,302,34,lda-rf,allround,12.87,6.02,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hepatitis,155,19,2,139,16,lda-rf,allround,15.3,9.11,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley noise,1212,100,2,606,606,lda-rf,allround,15.66,0.92,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley no noise,1212,100,2,606,606,lda-rf,allround,0.22,0.8,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,iris,150,4,3,135,15,lda-rf,allround,4.63,5.06,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,letters,"20,000",16,26,15000,"5,000",lda-rf,allround,3.34,0.15,default,100,sqrt(p),15 runs 75/25,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,movement,360,90,25,324,36,lda-rf,allround,15.13,5.63,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,lymphoma,298,18,4,268,30,lda-rf,allround,1.05,2.63,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,musk,467,167,2,420,47,lda-rf,allround,1.81,2.09,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,nursery,"12,960",8,5,11664,"1,296",lda-rf,allround,0.19,0.13,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,orl,400,1024,40,200,200,lda-rf,allround,10.7,2.07,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ozone,2536,72,2,2282,254,lda-rf,allround,2.89,1.1,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,page blocks,5473,10,5,4925,548,lda-rf,allround,2.35,0.62,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,parkinsons,195,22,2,175,20,lda-rf,allround,9.89,6.93,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,pie,"11,560",1024,68,2040,"9,520",lda-rf,allround,21.11,0.7,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,planning relax,182,12,2,163,19,lda-rf,allround,31.25,9.61,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,polyandenylation,9255,169,2,4418,4837,lda-rf,allround,24.61,0.73,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,seeds,210,7,3,189,21,lda-rf,allround,6.04,4.5,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,smpred,"12,563",100,2,2614,"9,949",lda-rf,allround,5.57,0.2,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,soybean,683,35,19,307,376,lda-rf,allround,5.03,4.08,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,spect heart,267,22,2,80,187,lda-rf,allround,22.07,1.02,default,100,sqrt(p),15 runs 33/66,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,splice,3190,59,2,2871,319,lda-rf,allround,7.9,1.79,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,steel plates faults,1941,27,7,1746,195,lda-rf,allround,22.66,3.08,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,heart,270,13,2,243,27,lda-rf,allround,16.77,6.86,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vehicle,846,18,4,761,85,lda-rf,allround,20.66,4.6,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 2c,310,6,2,279,31,lda-rf,allround,14.95,6.69,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 3c,310,6,3,279,31,lda-rf,allround,14.67,5.69,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform1,5000,21,3,4500,500,lda-rf,allround,14.12,1.5,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform2,5000,40,3,4500,500,lda-rf,allround,14.12,1.54,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine,178,13,3,160,18,lda-rf,allround,1.89,3.2,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality red,1599,11,6,1439,160,lda-rf,allround,29,3.7,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality white,4898,11,7,4408,490,lda-rf,allround,29.71,2.01,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yeast,1484,8,10,1335,149,lda-rf,allround,37.77,3.69,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yale,165,1024,15,75,90,lda-rf,allround,27.6,2.68,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yaleB,2432,1024,38,1216,1216,lda-rf,allround,6.92,1.21,default,100,sqrt(p),15 runs 40/60,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,afp,9974,19,2,8976,998,rf-ensemble,allround,21.59,0.63,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,balance,625,4,3,562,63,rf-ensemble,allround,11.91,3.41,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,biodeg,1055,41,2,949,106,rf-ensemble,allround,12.67,2.96,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer alt,699,9,2,629,70,rf-ensemble,allround,2.84,1.91,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,breast cancer,106,9,6,95,11,rf-ensemble,allround,28,13.93,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,car,1728,6,4,1555,173,rf-ensemble,allround,2.77,1.29,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,climate,540,18,2,486,54,rf-ensemble,allround,2.72,3.42,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ecoli,336,7,8,302,34,rf-ensemble,allround,13.26,5.81,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hepatitis,155,19,2,139,16,rf-ensemble,allround,15.18,8.7,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley noise,1212,100,2,606,606,rf-ensemble,allround,11.25,1.37,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,hillvalley no noise,1212,100,2,606,606,rf-ensemble,allround,0.17,0,default,100,sqrt(p),150 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,iris,150,4,3,135,15,rf-ensemble,allround,4.61,5.21,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,letters,"20,000",16,26,15000,"5,000",rf-ensemble,allround,3.57,0.13,default,100,sqrt(p),15 runs 75/25,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,movement,360,90,25,324,36,rf-ensemble,allround,14.1,5.69,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,lymphoma,298,18,4,268,30,rf-ensemble,allround,1,2.5,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,musk,467,167,2,420,47,rf-ensemble,allround,1.11,1.5,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,nursery,"12,960",8,5,11664,"1,296",rf-ensemble,allround,0.26,0.18,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,orl,400,1024,40,200,200,rf-ensemble,allround,10.6,2.04,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,ozone,2536,72,2,2282,254,rf-ensemble,allround,2.9,1.1,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,page blocks,5473,10,5,4925,548,rf-ensemble,allround,2.4,0.62,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,parkinsons,195,22,2,175,20,rf-ensemble,allround,8.77,6.38,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,pie,"11,560",1024,68,2040,"9,520",rf-ensemble,allround,18.68,0.83,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,planning relax,182,12,2,163,19,rf-ensemble,allround,30.87,9.67,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,polyandenylation,9255,169,2,4418,4837,rf-ensemble,allround,26.35,0.96,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,seeds,210,7,3,189,21,rf-ensemble,allround,6.06,4.8,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,smpred,"12,563",100,2,2614,"9,949",rf-ensemble,allround,4.67,0.11,default,100,sqrt(p),15 runs 20/80,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,soybean,683,35,19,307,376,rf-ensemble,allround,5.07,4.32,default,100,sqrt(p),15 runs 50/50,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,spect heart,267,22,2,80,187,rf-ensemble,allround,22.5,1.29,default,100,sqrt(p),15 runs 33/66,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,splice,3190,59,2,2871,319,rf-ensemble,allround,5.78,1.36,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,steel plates faults,1941,27,7,1746,195,rf-ensemble,allround,21.84,3.06,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,heart,270,13,2,243,27,rf-ensemble,allround,16.62,6.92,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vehicle,846,18,4,761,85,rf-ensemble,allround,21.77,4.45,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 2c,310,6,2,279,31,rf-ensemble,allround,14.99,6.78,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,vertebral 3c,310,6,3,279,31,rf-ensemble,allround,14.88,5.82,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform1,5000,21,3,4500,500,rf-ensemble,allround,13.78,1.51,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,waveform2,5000,40,3,4500,500,rf-ensemble,allround,13.79,1.6,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine,178,13,3,160,18,rf-ensemble,allround,2,3.37,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality red,1599,11,6,1439,160,rf-ensemble,allround,28.94,3.48,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,wine quality white,4898,11,7,4408,490,rf-ensemble,allround,29.91,2.06,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yeast,1484,8,10,1335,149,rf-ensemble,allround,37.92,3.65,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yale,165,1024,15,75,90,rf-ensemble,allround,28.54,5.07,default,100,sqrt(p),15 runs 10-cv,paired t-test
Random forests with ensemble of feature spaces,Zhang and Suganthan,2014,Pattern Recognition,yaleB,2432,1024,38,1216,1216,rf-ensemble,allround,2.84,0.37,default,100,sqrt(p),15 runs 40/60,paired t-test
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,colon,62,2000,2,40,22,xrf,HD,12.35,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,srbct,63,2308,4,41,22,xrf,HD,2.29,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,leukemia,38,3051,2,25,13,xrf,HD,10.75,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,lymphoma,62,4026,3,40,22,xrf,HD,0.7,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.2.class,78,4869,2,51,27,xrf,HD,21.16,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.3.class,96,4869,3,63,33,xrf,HD,34.58,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,nci60,61,"5,244",8,40,21,xrf,HD,25.85,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,brain,42,"5,597",5,27,15,xrf,HD,18.07,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,prostate,102,"6,033",2,67,35,xrf,HD,7.44,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,adenocarcinoma,76,"9,868",2,50,26,xrf,HD,9.12,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,fbis,2463,"2,000",17,"1,711",752,xrf,HD,15.31,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la2s,2700,"12,432",6,"1,855",845,xrf,HD,11.39,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la1s,2850,"13,195",6,"1,963",887,xrf,HD,12.79,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,colon,62,2000,2,40,22,rf,HD,15.65,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,srbct,63,2308,4,41,22,rf,HD,4.1,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,leukemia,38,3051,2,25,13,rf,HD,17.42,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,lymphoma,62,4026,3,40,22,rf,HD,2.85,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.2.class,78,4869,2,51,27,rf,HD,37.28,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.3.class,96,4869,3,63,33,rf,HD,44,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,nci60,61,"5,244",8,40,21,rf,HD,41.15,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,brain,42,"5,597",5,27,15,rf,HD,29.21,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,prostate,102,"6,033",2,67,35,rf,HD,11.29,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,adenocarcinoma,76,"9,868",2,50,26,rf,HD,15.96,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,fbis,2463,"2,000",17,"1,711",752,rf,HD,23.58,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la2s,2700,"12,432",6,"1,855",845,rf,HD,33.23,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la1s,2850,"13,195",6,"1,963",887,rf,HD,22.24,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,colon,62,2000,2,40,22,grrf,HD,13.55,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,srbct,63,2308,4,41,22,grrf,HD,2.43,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,leukemia,38,3051,2,25,13,grrf,HD,12.75,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,lymphoma,62,4026,3,40,22,grrf,HD,0.9,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.2.class,78,4869,2,51,27,grrf,HD,28.68,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.3.class,96,4869,3,63,33,grrf,HD,36.45,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,nci60,61,"5,244",8,40,21,grrf,HD,36.95,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,brain,42,"5,597",5,27,15,grrf,HD,25.21,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,prostate,102,"6,033",2,67,35,grrf,HD,7.15,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,adenocarcinoma,76,"9,868",2,50,26,grrf,HD,14.48,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,fbis,2463,"2,000",17,"1,711",752,grrf,HD,23.49,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la2s,2700,"12,432",6,"1,855",845,grrf,HD,32.01,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la1s,2850,"13,195",6,"1,963",887,grrf,HD,19.51,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,colon,62,2000,2,40,22,wsrf,HD,15.5,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,srbct,63,2308,4,41,22,wsrf,HD,3.24,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,leukemia,38,3051,2,25,13,wsrf,HD,15.17,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,lymphoma,62,4026,3,40,22,wsrf,HD,1.9,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.2.class,78,4869,2,51,27,wsrf,HD,36.6,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.3.class,96,4869,3,63,33,wsrf,HD,42.81,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,nci60,61,"5,244",8,40,21,wsrf,HD,40.6,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,brain,42,"5,597",5,27,15,wsrf,HD,29.21,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,prostate,102,"6,033",2,67,35,wsrf,HD,9.21,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,adenocarcinoma,76,"9,868",2,50,26,wsrf,HD,15.88,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,fbis,2463,"2,000",17,"1,711",752,wsrf,HD,15.86,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la2s,2700,"12,432",6,"1,855",845,wsrf,HD,12.74,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,la1s,2850,"13,195",6,"1,963",887,wsrf,HD,13.97,NA,default,1000,log(p+1),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,colon,62,2000,2,40,22,varSelRF,select-HD,23.2,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,srbct,63,2308,4,41,22,varSelRF,select-HD,3.5,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,leukemia,38,3051,2,25,13,varSelRF,select-HD,10.7,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,lymphoma,62,4026,3,40,22,varSelRF,select-HD,2.2,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.2.class,78,4869,2,51,27,varSelRF,select-HD,38.6,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.3.class,96,4869,3,63,33,varSelRF,select-HD,41.8,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,nci60,61,"5,244",8,40,21,varSelRF,select-HD,41.8,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,brain,42,"5,597",5,27,15,varSelRF,select-HD,23.1,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,prostate,102,"6,033",2,67,35,varSelRF,select-HD,8.5,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,adenocarcinoma,76,"9,868",2,50,26,varSelRF,select-HD,21.2,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,colon,62,2000,2,40,22,lasso-rf,select-HD,18,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,srbct,63,2308,4,41,22,lasso-rf,select-HD,0.7,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,leukemia,38,3051,2,25,13,lasso-rf,select-HD,7.6,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,lymphoma,62,4026,3,40,22,lasso-rf,select-HD,0.9,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.2.class,78,4869,2,51,27,lasso-rf,select-HD,36.6,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,breast.3.class,96,4869,3,63,33,lasso-rf,select-HD,40,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,nci60,61,"5,244",8,40,21,lasso-rf,select-HD,39.6,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,brain,42,"5,597",5,27,15,lasso-rf,select-HD,25.9,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,prostate,102,"6,033",2,67,35,lasso-rf,select-HD,8.8,NA,default,1000,sqrt(p),10-fold cv,simple diff
Unbiased feature selection in learning random forests for high dimensional data,Nguyen et al.,2015,The Scientific World Journal,adenocarcinoma,76,"9,868",2,50,26,lasso-rf,select-HD,18.9,NA,default,1000,sqrt(p),10-fold cv,simple diff
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,5.269,NA,fixed,50,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,5.011,NA,fixed,50,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.96,NA,fixed,50,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.963,NA,fixed,50,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,5.246,NA,fixed,500,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.981,NA,fixed,500,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.904,NA,fixed,500,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.944,NA,fixed,500,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,5.227,NA,fixed,5000,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.975,NA,fixed,5000,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.86,NA,fixed,5000,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rf,allround,4.964,NA,fixed,5000,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.464,NA,fixed,50,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.237,NA,fixed,50,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.155,NA,fixed,50,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.077,NA,fixed,50,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.414,NA,fixed,500,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.226,NA,fixed,500,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.144,NA,fixed,500,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.096,NA,fixed,500,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.385,NA,fixed,5000,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.196,NA,fixed,5000,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.133,NA,fixed,5000,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-rf,allround,4.132,NA,fixed,5000,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.335,NA,fixed,50,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.281,NA,fixed,50,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.183,NA,fixed,50,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.238,NA,fixed,50,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.244,NA,fixed,500,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.157,NA,fixed,500,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.118,NA,fixed,500,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.114,NA,fixed,500,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.257,NA,fixed,5000,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.175,NA,fixed,5000,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.038,NA,fixed,5000,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,erf,allround,5.046,NA,fixed,5000,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,3.994,NA,fixed,50,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.101,NA,fixed,50,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.078,NA,fixed,50,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.152,NA,fixed,50,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,3.971,NA,fixed,500,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.045,NA,fixed,500,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.056,NA,fixed,500,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.111,NA,fixed,500,4,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.044,NA,fixed,5000,1,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.003,NA,fixed,5000,2,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.079,NA,fixed,5000,3,10000 runs 50/50,wilcoxon signed-rank
Random rotation ensembles,Blaser and Fryzlewicz,2015,Machine Learning,iris,150,4,3,75,75,rr-erf,allround,4.053,NA,fixed,5000,4,10000 runs 50/50,wilcoxon signed-rank
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,tree,allround,12.48,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,tree,allround,6.08,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,tree,allround,32.73,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,tree,allround,31.59,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,tree,allround,27.62,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,tree,allround,4.79,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,tree,allround,19.72,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,tree,allround,29.57,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,ann,allround,12.1,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,ann,allround,2.28,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,ann,allround,26.96,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,ann,allround,22.11,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,ann,allround,20.26,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,ann,allround,2.02,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,ann,allround,15.65,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,ann,allround,30.43,NA,NA,NA,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,soft tree,allround,11.97,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,soft tree,allround,2.12,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,soft tree,allround,25.53,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,soft tree,allround,20.78,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,soft tree,allround,13.33,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,soft tree,allround,2.27,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,soft tree,allround,16.59,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,soft tree,allround,30.43,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,dndt,allround,7.15,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,dndt,allround,1.89,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,dndt,allround,24.44,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,dndt,allround,20.56,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,dndt,allround,13.11,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,dndt,allround,1.94,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,dndt,allround,15.3,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,dndt,allround,22.55,NA,NA,1,NA,1 run train/test,simple diff
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,svm-radial,allround,4.27,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,svm-radial,allround,1.59,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,svm-radial,allround,26.12,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,svm-radial,allround,22.35,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,svm-radial,allround,21.11,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,svm-radial,allround,1.31,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,svm-radial,allround,14.85,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,svm-radial,allround,17.39,NA,grid search,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,boosting,allround,5.16,0.9,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,boosting,allround,2.38,0.42,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,boosting,allround,24.29,0.96,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,boosting,allround,23.29,0.99,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,boosting,allround,15,1.68,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,boosting,allround,1.39,0.05,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,boosting,allround,14.23,0.91,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,boosting,allround,18.49,1.64,none,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,orf-ridge,allround,5.69,0.56,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,orf-ridge,allround,1.63,0.21,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,orf-ridge,allround,25.1,0.94,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,orf-ridge,allround,23.25,0.85,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,orf-ridge,allround,15.07,0.9,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,orf-ridge,allround,1.68,0.02,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,orf-ridge,allround,16.14,0.46,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,orf-ridge,allround,24.12,1.75,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,orf-svm,allround,4.32,0.5,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,orf-svm,allround,1.58,0.07,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,orf-svm,allround,24.23,0.56,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,orf-svm,allround,23.25,0.81,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,orf-svm,allround,13.18,0.9,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,orf-svm,allround,1.87,0.04,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,orf-svm,allround,15.5,0.64,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,orf-svm,allround,27.86,1.74,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,orf-log,allround,4.5,0.62,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,orf-log,allround,1.74,0.26,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,orf-log,allround,25.23,0.82,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,orf-log,allround,22.43,1.26,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,orf-log,allround,15.27,0.99,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,orf-log,allround,1.62,0.05,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,orf-log,allround,15.84,0.59,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,orf-log,allround,24.52,1.92,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,rotationForest,allround,5.04,1.35,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,rotationForest,allround,1.82,0.76,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,rotationForest,allround,25.23,1.22,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,rotationForest,allround,21.63,1.42,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,rotationForest,allround,19.05,2.56,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,rotationForest,allround,2.14,0.24,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,rotationForest,allround,16.09,0.79,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,rotationForest,allround,23.12,3.4,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,rf,allround,6.99,0.94,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,rf,allround,2.15,0.41,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,rf,allround,24.71,0.82,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,rf,allround,23.92,0.64,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,rf,allround,15.11,1.14,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,rf,allround,2,0.04,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,rf,allround,15.81,0.74,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,rf,allround,25.48,2.01,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ionosphere,351,34,2,234,117,dnrf,allround,3.38,0.57,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,wdbc,569,30,2,380,189,dnrf,allround,0.53,0,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,german credit,1000,24,2,667,333,dnrf,allround,22.91,0.37,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pima,768,8,2,513,255,dnrf,allround,19.41,0.42,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,heart,270,13,2,180,90,dnrf,allround,12.22,0,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,ijcnn,141691,22,2,49990,91701,dnrf,allround,1.14,0.01,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,aus credit,690,14,2,461,229,dnrf,allround,13.56,0.44,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,sonar,208,60,2,139,69,dnrf,allround,18.14,1.5,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pendigits,10992,16,10,7494,3498,gd-MCboost,allround,7.06,NA,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,optdigit,5620,62,10,3823,1797,gd-MCboost,allround,7.68,NA,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,satellite,6435,36,6,4435,2000,gd-MCboost,allround,13.35,NA,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,letters,20000,16,26,16000,4000,gd-MCboost,allround,40.35,NA,NA,ntree-tuning,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pendigits,10992,16,10,7494,3498,space part,allround,4.32,NA,NA,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,optdigit,5620,62,10,3823,1797,space part,allround,4.23,NA,NA,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,satellite,6435,36,6,4435,2000,space part,allround,13.95,NA,NA,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,letters,20000,16,26,16000,4000,space part,allround,13.08,NA,NA,NA,NA,50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pendigits,10992,16,10,7494,3498,rf,allround,3.87,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,optdigit,5620,62,10,3823,1797,rf,allround,3.22,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,satellite,6435,36,6,4435,2000,rf,allround,10.49,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,letters,20000,16,26,16000,4000,rf,allround,4.7,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,pendigits,10992,16,10,7494,3498,dnrf,allround,2.17,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,optdigit,5620,62,10,3823,1797,dnrf,allround,2.3,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,satellite,6435,36,6,4435,2000,dnrf,allround,9.63,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Disjunctive normal random forests,Seyedhosseini and Tasdizen,2015,Pattern Recognition,letters,20000,16,26,16000,4000,dnrf,allround,2.05,NA,default,ntree-tuning,sqrt(p),50 runs train/test,paired t-test
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,breast cancer,699,9,2,461,238,rf,allround,4.8,1.7,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,ionosphere,351,34,2,231,120,rf,allround,6.5,2.2,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,sonar,208,60,2,137,71,rf,allround,21.5,5.1,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,satellite,6435,36,6,4247,2188,rf,allround,10.9,0.3,default,50,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,pima,768,8,2,506,262,rf,allround,24.5,2.1,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,heart,270,13,2,178,92,rf,allround,16.3,4.4,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,credit,690,15,2,455,235,rf,allround,25.6,2.2,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,breast cancer,699,9,2,461,238,ann,allround,3.4,1.7,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,ionosphere,351,34,2,231,120,ann,allround,11,3.3,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,sonar,208,60,2,137,71,ann,allround,18,5.5,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,satellite,6435,36,6,4247,2188,ann,allround,10.1,0.4,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,pima,768,8,2,506,262,ann,allround,28.9,3,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,heart,270,13,2,178,92,ann,allround,21.8,4.3,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,credit,690,15,2,455,235,ann,allround,28.3,2.7,fixed,NA,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,breast cancer,699,9,2,461,238,ann-rf-sparse,allround,2.9,1.3,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,ionosphere,351,34,2,231,120,ann-rf-sparse,allround,6.2,2,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,sonar,208,60,2,137,71,ann-rf-sparse,allround,14.4,4.8,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,satellite,6435,36,6,4247,2188,ann-rf-sparse,allround,9.1,0.3,default,50,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,pima,768,8,2,506,262,ann-rf-sparse,allround,26.8,3.1,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,heart,270,13,2,178,92,ann-rf-sparse,allround,19.5,4.1,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,credit,690,15,2,455,235,ann-rf-sparse,allround,24.6,1.8,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,breast cancer,699,9,2,461,238,ann-rf-relaxed,allround,3.5,1.9,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,ionosphere,351,34,2,231,120,ann-rf-relaxed,allround,6.9,2.7,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,sonar,208,60,2,137,71,ann-rf-relaxed,allround,16,5.2,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,satellite,6435,36,6,4247,2188,ann-rf-relaxed,allround,9.1,0.4,default,50,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,pima,768,8,2,506,262,ann-rf-relaxed,allround,26.4,2.3,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,heart,270,13,2,178,92,ann-rf-relaxed,allround,19.5,4.3,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,credit,690,15,2,455,235,ann-rf-relaxed,allround,24.7,2.9,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,breast cancer,699,9,2,461,238,ann-rf-vote,allround,4.1,1.8,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,ionosphere,351,34,2,231,120,ann-rf-vote,allround,6.7,1.9,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,sonar,208,60,2,137,71,ann-rf-vote,allround,14.8,4.1,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,satellite,6435,36,6,4247,2188,ann-rf-vote,allround,9,0.4,default,50,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,pima,768,8,2,506,262,ann-rf-vote,allround,26.6,2.6,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,heart,270,13,2,178,92,ann-rf-vote,allround,19,4.1,default,200,sqrt(p),30 runs train/test,simple diff
Casting Random Forests as Artificial Neural Networks ( and Profiting from It ),Welbl,2014,Pattern Recognition,credit,690,15,2,455,235,ann-rf-vote,allround,25.3,2.7,default,200,sqrt(p),30 runs train/test,simple diff
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,breast cancer,699,10,2,629,70,wave,allround,3.76,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,liver,345,6,2,310,35,wave,allround,27.85,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,body dimension,507,24,2,456,51,wave,allround,6.5,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,boston housing,506,14,3,455,51,wave,allround,22.33,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,contraceptive,1473,9,3,1325,148,wave,allround,43.87,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,horse,368,27,3,331,37,wave,allround,29.11,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,credit,690,15,2,621,69,wave,allround,13.74,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,cylinder,540,35,2,486,54,wave,allround,25.12,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,dermatology,366,33,6,329,37,wave,allround,4.65,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,diabetes,532,7,2,478,54,wave,allround,23.84,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,fish,159,7,7,143,16,wave,allround,17.53,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,german credit,1000,20,2,900,100,wave,allround,24.38,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,glass,214,9,6,192,22,wave,allround,24.3,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,heart,270,13,2,243,27,wave,allround,17.29,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,chess,1000,10,2,900,100,wave,allround,17.74,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,ionosphere,351,34,2,315,36,wave,allround,8.92,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,iris,150,4,3,135,15,wave,allround,4.47,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,lakes,259,16,6,233,26,wave,allround,57.57,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,LED ,6000,7,10,5400,600,wave,allround,28.31,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,pima,768,8,2,691,77,wave,allround,21.99,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,poverty,97,6,6,87,10,wave,allround,32.7,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,harp,3000,7,3,2700,300,wave,allround,40.62,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,spectf heart,267,44,2,240,27,wave,allround,19.53,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,usnews,1302,27,3,1171,131,wave,allround,24.95,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vehicle,946,18,4,851,95,wave,allround,28.54,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,volcano,1521,6,6,1368,153,wave,allround,11.62,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,votes,435,16,2,391,44,wave,allround,4.57,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vowel,990,10,11,891,99,wave,allround,29.22,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,breast cancer,699,10,2,629,70,bagging,allround,3.83,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,liver,345,6,2,310,35,bagging,allround,27.87,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,body dimension,507,24,2,456,51,bagging,allround,6.63,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,boston housing,506,14,3,455,51,bagging,allround,22.41,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,contraceptive,1473,9,3,1325,148,bagging,allround,43.88,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,horse,368,27,3,331,37,bagging,allround,29.22,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,credit,690,15,2,621,69,bagging,allround,13.75,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,cylinder,540,35,2,486,54,bagging,allround,25.29,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,dermatology,366,33,6,329,37,bagging,allround,5.03,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,diabetes,532,7,2,478,54,bagging,allround,23.82,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,fish,159,7,7,143,16,bagging,allround,17.54,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,german credit,1000,20,2,900,100,bagging,allround,24.43,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,glass,214,9,6,192,22,bagging,allround,24.32,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,heart,270,13,2,243,27,bagging,allround,17.24,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,chess,1000,10,2,900,100,bagging,allround,20.38,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,ionosphere,351,34,2,315,36,bagging,allround,9.06,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,iris,150,4,3,135,15,bagging,allround,4.65,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,lakes,259,16,6,233,26,bagging,allround,57.63,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,LED ,6000,7,10,5400,600,bagging,allround,28.58,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,pima,768,8,2,691,77,bagging,allround,21.95,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,poverty,97,6,6,87,10,bagging,allround,32.61,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,harp,3000,7,3,2700,300,bagging,allround,40.77,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,spectf heart,267,44,2,240,27,bagging,allround,19.46,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,usnews,1302,27,3,1171,131,bagging,allround,25,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vehicle,946,18,4,851,95,bagging,allround,28.61,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,volcano,1521,6,6,1368,153,bagging,allround,11.88,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,votes,435,16,2,391,44,bagging,allround,4.56,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vowel,990,10,11,891,99,bagging,allround,29.38,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,breast cancer,699,10,2,629,70,boosting,allround,3.84,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,liver,345,6,2,310,35,boosting,allround,27.69,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,body dimension,507,24,2,456,51,boosting,allround,2.18,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,boston housing,506,14,3,455,51,boosting,allround,21.78,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,contraceptive,1473,9,3,1325,148,boosting,allround,45.35,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,horse,368,27,3,331,37,boosting,allround,31.73,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,credit,690,15,2,621,69,boosting,allround,14.91,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,cylinder,540,35,2,486,54,boosting,allround,24.89,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,dermatology,366,33,6,329,37,boosting,allround,3.08,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,diabetes,532,7,2,478,54,boosting,allround,24.38,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,fish,159,7,7,143,16,boosting,allround,17.68,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,german credit,1000,20,2,900,100,boosting,allround,24.94,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,glass,214,9,6,192,22,boosting,allround,22.07,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,heart,270,13,2,243,27,boosting,allround,21.03,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,chess,1000,10,2,900,100,boosting,allround,36.94,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,ionosphere,351,34,2,315,36,boosting,allround,7.63,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,iris,150,4,3,135,15,boosting,allround,4.83,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,lakes,259,16,6,233,26,boosting,allround,59.75,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,LED ,6000,7,10,5400,600,boosting,allround,29.4,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,pima,768,8,2,691,77,boosting,allround,23.4,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,poverty,97,6,6,87,10,boosting,allround,38.55,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,harp,3000,7,3,2700,300,boosting,allround,35.41,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,spectf heart,267,44,2,240,27,boosting,allround,20.23,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,usnews,1302,27,3,1171,131,boosting,allround,28.31,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vehicle,946,18,4,851,95,boosting,allround,25.36,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,volcano,1521,6,6,1368,153,boosting,allround,11.33,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,votes,435,16,2,391,44,boosting,allround,4.49,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vowel,990,10,11,891,99,boosting,allround,5.79,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,breast cancer,699,10,2,629,70,boosting,allround,3.84,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,liver,345,6,2,310,35,boosting,allround,27.69,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,body dimension,507,24,2,456,51,boosting,allround,2.18,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,boston housing,506,14,3,455,51,boosting,allround,21.78,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,contraceptive,1473,9,3,1325,148,boosting,allround,45.35,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,horse,368,27,3,331,37,boosting,allround,31.73,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,credit,690,15,2,621,69,boosting,allround,14.91,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,cylinder,540,35,2,486,54,boosting,allround,24.89,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,dermatology,366,33,6,329,37,boosting,allround,3.08,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,diabetes,532,7,2,478,54,boosting,allround,24.38,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,fish,159,7,7,143,16,boosting,allround,17.68,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,german credit,1000,20,2,900,100,boosting,allround,24.94,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,glass,214,9,6,192,22,boosting,allround,22.07,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,heart,270,13,2,243,27,boosting,allround,21.03,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,chess,1000,10,2,900,100,boosting,allround,36.94,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,ionosphere,351,34,2,315,36,boosting,allround,7.63,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,iris,150,4,3,135,15,boosting,allround,4.83,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,lakes,259,16,6,233,26,boosting,allround,59.75,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,LED ,6000,7,10,5400,600,boosting,allround,29.4,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,pima,768,8,2,691,77,boosting,allround,23.4,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,poverty,97,6,6,87,10,boosting,allround,38.55,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,harp,3000,7,3,2700,300,boosting,allround,35.41,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,spectf heart,267,44,2,240,27,boosting,allround,20.23,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,usnews,1302,27,3,1171,131,boosting,allround,28.31,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vehicle,946,18,4,851,95,boosting,allround,25.36,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,volcano,1521,6,6,1368,153,boosting,allround,11.33,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,votes,435,16,2,391,44,boosting,allround,4.49,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vowel,990,10,11,891,99,boosting,allround,5.79,NA,none,64,NA,100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,breast cancer,699,10,2,629,70,rf,allround,2.92,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,liver,345,6,2,310,35,rf,allround,27.49,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,body dimension,507,24,2,456,51,rf,allround,5.8,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,boston housing,506,14,3,455,51,rf,allround,21.67,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,contraceptive,1473,9,3,1325,148,rf,allround,44.97,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,horse,368,27,3,331,37,rf,allround,28.77,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,credit,690,15,2,621,69,rf,allround,13.65,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,cylinder,540,35,2,486,54,rf,allround,26.28,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,dermatology,366,33,6,329,37,rf,allround,2.45,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,diabetes,532,7,2,478,54,rf,allround,23.86,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,fish,159,7,7,143,16,rf,allround,18.93,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,german credit,1000,20,2,900,100,rf,allround,25.33,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,glass,214,9,6,192,22,rf,allround,23.21,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,heart,270,13,2,243,27,rf,allround,16.7,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,chess,1000,10,2,900,100,rf,allround,46.6,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,ionosphere,351,34,2,315,36,rf,allround,7.35,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,iris,150,4,3,135,15,rf,allround,4.39,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,lakes,259,16,6,233,26,rf,allround,57.71,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,LED ,6000,7,10,5400,600,rf,allround,27.48,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,pima,768,8,2,691,77,rf,allround,22.11,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,poverty,97,6,6,87,10,rf,allround,37.07,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,harp,3000,7,3,2700,300,rf,allround,39.27,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,spectf heart,267,44,2,240,27,rf,allround,19.24,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,usnews,1302,27,3,1171,131,rf,allround,26.42,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vehicle,946,18,4,851,95,rf,allround,28.17,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,volcano,1521,6,6,1368,153,rf,allround,12.56,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,votes,435,16,2,391,44,rf,allround,4.3,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vowel,990,10,11,891,99,rf,allround,27.27,NA,default,64,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,breast cancer,699,10,2,629,70,tree-cart,allround,5.5,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,liver,345,6,2,310,35,tree-cart,allround,31.85,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,body dimension,507,24,2,456,51,tree-cart,allround,8.57,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,housing,506,14,3,455,51,tree-cart,allround,25.9,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,contraceptive,1473,9,3,1325,148,tree-cart,allround,44.96,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,horse,368,27,3,331,37,tree-cart,allround,33.94,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,credit,690,15,2,621,69,tree-cart,allround,14.79,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,cylinder,540,35,2,486,54,tree-cart,allround,35.39,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,dermatology,366,33,6,329,37,tree-cart,allround,6.64,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,diabetes,532,7,2,478,54,tree-cart,allround,25.22,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,fish,159,7,7,143,16,tree-cart,allround,19.69,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,german credit,1000,20,2,900,100,tree-cart,allround,27.48,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,glass,214,9,6,192,22,tree-cart,allround,29.83,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,heart,270,13,2,243,27,tree-cart,allround,22.34,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,chess,1000,10,2,900,100,tree-cart,allround,19.99,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,ionosphere,351,34,2,315,36,tree-cart,allround,12.72,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,iris,150,4,3,135,15,tree-cart,allround,4.5,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,lakes,259,16,6,233,26,tree-cart,allround,63.27,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,LED ,6000,7,10,5400,600,tree-cart,allround,31.81,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,pima,768,8,2,691,77,tree-cart,allround,25.1,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,poverty,97,6,6,87,10,tree-cart,allround,34.43,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,harp,3000,7,3,2700,300,tree-cart,allround,41.91,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,spectf heart,267,44,2,240,27,tree-cart,allround,26.19,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,usnews,1302,27,3,1171,131,tree-cart,allround,26.83,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vehicle,946,18,4,851,95,tree-cart,allround,31.82,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,volcano,1521,6,6,1368,153,tree-cart,allround,12.83,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,votes,435,16,2,391,44,tree-cart,allround,5.71,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
A weight-adjusted voting algorithm for ensembles of classifiers,Kim et al.,2011,Journal of the Korean Statistical Society,vowel,990,10,11,891,99,tree-cart,allround,39.86,NA,none,1,sqrt(p),100 runs 10-cv,paired t-tests and Wilcoxon signed rank
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,abalone,4177,8,3,2756,1421,RotBoost,allround,35.63,0.48,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,aus credit,690,14,2,455,235,RotBoost,allround,13.46,0.79,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,balance,625,4,3,412,213,RotBoost,allround,11.26,1.37,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer alt,286,9,2,188,98,RotBoost,allround,29.19,1.6,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer,683,9,2,450,233,RotBoost,allround,3.21,0.37,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,car,1748,6,4,1153,595,RotBoost,allround,3.94,0.37,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,cleveland heart,297,13,5,196,101,RotBoost,allround,43.8,1.28,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,contraceptive,1473,9,3,972,501,RotBoost,allround,47.68,0.98,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,credit,653,15,2,430,223,RotBoost,allround,12.66,0.75,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,dermatology,366,34,6,241,125,RotBoost,allround,2.8,0.61,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ecoli,336,7,8,221,115,RotBoost,allround,15.37,1.02,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,german credit,1000,24,2,660,340,RotBoost,allround,24.56,0.85,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,glass,214,10,7,141,73,RotBoost,allround,24.46,0.87,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,haberman,306,3,2,201,105,RotBoost,allround,31.37,2.13,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,heart,270,13,2,178,92,RotBoost,allround,18.67,1.45,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,hepatitis,80,19,2,52,28,RotBoost,allround,32.75,4.81,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,housing,506,13,3,333,173,RotBoost,allround,21.07,1.36,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ionosphere,351,34,2,231,120,RotBoost,allround,5.57,0.76,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,iris,150,4,3,99,51,RotBoost,allround,4.53,0.91,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,liver,345,6,2,227,118,RotBoost,allround,30.35,1.81,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,lymphoma,148,18,4,97,51,RotBoost,allround,26.05,13.44,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pendigits,10992,16,10,7254,3738,RotBoost,allround,0.62,0.06,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pima,768,8,2,506,262,RotBoost,allround,24.62,1.19,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,segment,2310,19,7,1524,786,RotBoost,allround,1.83,0.19,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,sonar,208,60,2,137,71,RotBoost,allround,17.23,2.16,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,soybean,683,35,19,450,233,RotBoost,allround,6.5,0.78,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spect,267,4,2,176,91,RotBoost,allround,17.98,1.17,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spectf heart,269,44,2,177,92,RotBoost,allround,19.66,1.57,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vehicle,846,18,4,558,288,RotBoost,allround,21.92,1.09,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,votes,435,16,2,287,148,RotBoost,allround,4.74,0.49,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vowel,990,11,11,653,337,RotBoost,allround,5.33,0.71,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wdbc,569,30,2,375,194,RotBoost,allround,3.22,0.37,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wine,178,13,3,117,61,RotBoost,allround,3.06,0.67,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wpbc,194,32,2,128,66,RotBoost,allround,25.49,1.47,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,yeast,1484,8,10,979,505,RotBoost,allround,39.78,1.05,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,zoo,101,16,7,66,35,RotBoost,allround,5.49,1.83,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,abalone,4177,8,3,2756,1421,tree-cart,allround,41.79,0.89,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,aus credit,690,14,2,455,235,tree-cart,allround,16.97,1.12,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,balance,625,4,3,412,213,tree-cart,allround,21.72,1.19,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer alt,286,9,2,188,98,tree-cart,allround,33.78,1.65,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer,683,9,2,450,233,tree-cart,allround,5.53,0.71,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,car,1748,6,4,1153,595,tree-cart,allround,5.35,0.51,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,cleveland heart,297,13,5,196,101,tree-cart,allround,47.96,2.08,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,contraceptive,1473,9,3,972,501,tree-cart,allround,49.23,1.13,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,credit,653,15,2,430,223,tree-cart,allround,16.78,1.21,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,dermatology,366,34,6,241,125,tree-cart,allround,5.37,0.81,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ecoli,336,7,8,221,115,tree-cart,allround,19.82,1.56,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,german credit,1000,24,2,660,340,tree-cart,allround,35.49,1.37,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,glass,214,10,7,141,73,tree-cart,allround,24.49,0.84,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,haberman,306,3,2,201,105,tree-cart,allround,32.93,3.27,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,heart,270,13,2,178,92,tree-cart,allround,31.26,2.72,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,hepatitis,80,19,2,52,28,tree-cart,allround,41.25,4.42,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,housing,506,13,3,333,173,tree-cart,allround,27.15,1.46,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ionosphere,351,34,2,231,120,tree-cart,allround,12.37,1.18,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,iris,150,4,3,99,51,tree-cart,allround,5.63,1.32,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,liver,345,6,2,227,118,tree-cart,allround,35.85,2.31,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,lymphoma,148,18,4,97,51,tree-cart,allround,32.91,11.07,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pendigits,10992,16,10,7254,3738,tree-cart,allround,4.75,0.23,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pima,768,8,2,506,262,tree-cart,allround,29.4,1.23,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,segment,2310,19,7,1524,786,tree-cart,allround,4.87,0.34,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,sonar,208,60,2,137,71,tree-cart,allround,30.1,2.36,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,soybean,683,35,19,450,233,tree-cart,allround,11.36,1.31,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spect,267,4,2,176,91,tree-cart,allround,20.24,2.17,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spectf heart,269,44,2,177,92,tree-cart,allround,25.82,2.44,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vehicle,846,18,4,558,288,tree-cart,allround,30.68,1.25,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,votes,435,16,2,287,148,tree-cart,allround,5.41,0.66,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vowel,990,11,11,653,337,tree-cart,allround,27.41,1.4,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wdbc,569,30,2,375,194,tree-cart,allround,7.57,0.98,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wine,178,13,3,117,61,tree-cart,allround,9.38,1.77,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wpbc,194,32,2,128,66,tree-cart,allround,35.34,2.98,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,yeast,1484,8,10,979,505,tree-cart,allround,47.68,1.22,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,zoo,101,16,7,66,35,tree-cart,allround,12.37,3.68,none,1,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,abalone,4177,8,3,2756,1421,bagging,allround,36.26,0.44,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,aus credit,690,14,2,455,235,bagging,allround,13.09,0.54,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,balance,625,4,3,412,213,bagging,allround,15.03,0.93,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer alt,286,9,2,188,98,bagging,allround,29.06,1.37,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer,683,9,2,450,233,bagging,allround,3.45,0.34,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,car,1748,6,4,1153,595,bagging,allround,3.91,0.27,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,cleveland heart,297,13,5,196,101,bagging,allround,44.09,2.05,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,contraceptive,1473,9,3,972,501,bagging,allround,46.14,1.11,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,credit,653,15,2,430,223,bagging,allround,13.38,1.01,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,dermatology,366,34,6,241,125,bagging,allround,3.6,0.78,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ecoli,336,7,8,221,115,bagging,allround,17.23,1.36,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,german credit,1000,24,2,660,340,bagging,allround,28.24,0.7,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,glass,214,10,7,141,73,bagging,allround,24.53,0.92,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,haberman,306,3,2,201,105,bagging,allround,29.75,1.76,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,heart,270,13,2,178,92,bagging,allround,23.28,1.67,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,hepatitis,80,19,2,52,28,bagging,allround,33.62,2.63,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,housing,506,13,3,333,173,bagging,allround,21.9,1.26,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ionosphere,351,34,2,231,120,bagging,allround,8.46,0.74,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,iris,150,4,3,99,51,bagging,allround,4.7,1.23,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,liver,345,6,2,227,118,bagging,allround,29.8,2.13,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,lymphoma,148,18,4,97,51,bagging,allround,33.92,10.93,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pendigits,10992,16,10,7254,3738,bagging,allround,1.99,0.08,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pima,768,8,2,506,262,bagging,allround,24.04,0.77,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,segment,2310,19,7,1524,786,bagging,allround,3.25,0.24,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,sonar,208,60,2,137,71,bagging,allround,23.08,1.82,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,soybean,683,35,19,450,233,bagging,allround,8.38,0.81,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spect,267,4,2,176,91,bagging,allround,17.08,1.5,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spectf heart,269,44,2,177,92,bagging,allround,19.54,1.33,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vehicle,846,18,4,558,288,bagging,allround,25.79,0.97,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,votes,435,16,2,287,148,bagging,allround,4.57,0.64,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vowel,990,11,11,653,337,bagging,allround,13.62,0.82,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wdbc,569,30,2,375,194,bagging,allround,4.84,0.48,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wine,178,13,3,117,61,bagging,allround,4.55,1.77,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wpbc,194,32,2,128,66,bagging,allround,25.93,2,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,yeast,1484,8,10,979,505,bagging,allround,40.25,0.82,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,zoo,101,16,7,66,35,bagging,allround,10.74,3,none,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,abalone,4177,8,3,2756,1421,adaboost,allround,37.1,0.59,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,aus credit,690,14,2,455,235,adaboost,allround,13.66,0.75,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,balance,625,4,3,412,213,adaboost,allround,21.62,0.75,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer alt,286,9,2,188,98,adaboost,allround,32.22,2.17,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer,683,9,2,450,233,adaboost,allround,3.15,0.35,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,car,1748,6,4,1153,595,adaboost,allround,4.01,0.27,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,cleveland heart,297,13,5,196,101,adaboost,allround,45.55,1.97,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,contraceptive,1473,9,3,972,501,adaboost,allround,48.52,0.84,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,credit,653,15,2,430,223,adaboost,allround,12.7,0.72,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,dermatology,366,34,6,241,125,adaboost,allround,3.14,0.8,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ecoli,336,7,8,221,115,adaboost,allround,15.7,1.16,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,german credit,1000,24,2,660,340,adaboost,allround,25.38,0.89,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,glass,214,10,7,141,73,adaboost,allround,24.56,0.88,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,haberman,306,3,2,201,105,adaboost,allround,33.76,2.09,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,heart,270,13,2,178,92,adaboost,allround,21.91,2.16,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,hepatitis,80,19,2,52,28,adaboost,allround,36.62,5.1,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,housing,506,13,3,333,173,adaboost,allround,21.56,1.16,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ionosphere,351,34,2,231,120,adaboost,allround,6.3,0.77,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,iris,150,4,3,99,51,adaboost,allround,5.33,1.08,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,liver,345,6,2,227,118,adaboost,allround,30.59,1.48,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,lymphoma,148,18,4,97,51,adaboost,allround,29.16,13.66,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pendigits,10992,16,10,7254,3738,adaboost,allround,0.65,0.06,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pima,768,8,2,506,262,adaboost,allround,25.74,0.9,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,segment,2310,19,7,1524,786,adaboost,allround,1.66,0.25,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,sonar,208,60,2,137,71,adaboost,allround,17.98,2.14,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,soybean,683,35,19,450,233,adaboost,allround,6.59,0.79,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spect,267,4,2,176,91,adaboost,allround,18.45,0.91,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spectf heart,269,44,2,177,92,adaboost,allround,19.42,1.38,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vehicle,846,18,4,558,288,adaboost,allround,23.68,1.06,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,votes,435,16,2,287,148,adaboost,allround,5.08,0.66,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vowel,990,11,11,653,337,adaboost,allround,6.25,0.76,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wdbc,569,30,2,375,194,adaboost,allround,3.53,0.56,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wine,178,13,3,117,61,adaboost,allround,3.31,0.81,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wpbc,194,32,2,128,66,adaboost,allround,26.37,1.69,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,yeast,1484,8,10,979,505,adaboost,allround,41.27,0.92,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,zoo,101,16,7,66,35,adaboost,allround,6.44,2.73,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,abalone,4177,8,3,2756,1421,multiBoost,allround,36.23,0.42,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,aus credit,690,14,2,455,235,multiBoost,allround,13.22,0.82,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,balance,625,4,3,412,213,multiBoost,allround,18.03,0.83,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer alt,286,9,2,188,98,multiBoost,allround,30.8,1.24,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer,683,9,2,450,233,multiBoost,allround,3.13,0.3,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,car,1748,6,4,1153,595,multiBoost,allround,3.91,0.41,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,cleveland heart,297,13,5,196,101,multiBoost,allround,44.48,1.73,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,contraceptive,1473,9,3,972,501,multiBoost,allround,47.2,0.91,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,credit,653,15,2,430,223,multiBoost,allround,13.25,0.77,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,dermatology,366,34,6,241,125,multiBoost,allround,3.02,0.79,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ecoli,336,7,8,221,115,multiBoost,allround,14.99,1.16,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,german credit,1000,24,2,660,340,multiBoost,allround,24.87,0.72,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,glass,214,10,7,141,73,multiBoost,allround,24.37,0.83,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,haberman,306,3,2,201,105,multiBoost,allround,32.06,2.01,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,heart,270,13,2,178,92,multiBoost,allround,20.8,1.44,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,hepatitis,80,19,2,52,28,multiBoost,allround,35.69,4.62,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,housing,506,13,3,333,173,multiBoost,allround,21.18,1.04,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ionosphere,351,34,2,231,120,multiBoost,allround,6.27,0.8,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,iris,150,4,3,99,51,multiBoost,allround,5.8,1.15,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,liver,345,6,2,227,118,multiBoost,allround,29.04,2.07,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,lymphoma,148,18,4,97,51,multiBoost,allround,23.92,13.19,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pendigits,10992,16,10,7254,3738,multiBoost,allround,0.71,0.05,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pima,768,8,2,506,262,multiBoost,allround,24.79,0.76,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,segment,2310,19,7,1524,786,multiBoost,allround,1.79,0.19,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,sonar,208,60,2,137,71,multiBoost,allround,18.63,2.23,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,soybean,683,35,19,450,233,multiBoost,allround,6.54,0.65,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spect,267,4,2,176,91,multiBoost,allround,18.67,0.9,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spectf heart,269,44,2,177,92,multiBoost,allround,18.72,2.03,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vehicle,846,18,4,558,288,multiBoost,allround,23.65,0.89,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,votes,435,16,2,287,148,multiBoost,allround,4.81,0.72,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vowel,990,11,11,653,337,multiBoost,allround,7.28,1.27,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wdbc,569,30,2,375,194,multiBoost,allround,3.44,0.52,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wine,178,13,3,117,61,multiBoost,allround,3.23,1.01,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wpbc,194,32,2,128,66,multiBoost,allround,25.82,1.56,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,yeast,1484,8,10,979,505,multiBoost,allround,40.04,0.68,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,zoo,101,16,7,66,35,multiBoost,allround,7.28,2.91,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,abalone,4177,8,3,2756,1421,rotationForest,allround,34.98,0.27,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,aus credit,690,14,2,455,235,rotationForest,allround,13.66,0.78,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,balance,625,4,3,412,213,rotationForest,allround,9.62,0.72,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer alt,286,9,2,188,98,rotationForest,allround,30.44,2.97,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,breast cancer,683,9,2,450,233,rotationForest,allround,2.89,0.33,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,car,1748,6,4,1153,595,rotationForest,allround,3.63,0.29,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,cleveland heart,297,13,5,196,101,rotationForest,allround,44.38,1.47,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,contraceptive,1473,9,3,972,501,rotationForest,allround,46.97,0.78,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,credit,653,15,2,430,223,rotationForest,allround,13.18,0.66,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,dermatology,366,34,6,241,125,rotationForest,allround,2.62,0.68,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ecoli,336,7,8,221,115,rotationForest,allround,16.37,1.33,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,german credit,1000,24,2,660,340,rotationForest,allround,28.47,0.83,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,glass,214,10,7,141,73,rotationForest,allround,24.39,0.56,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,haberman,306,3,2,201,105,rotationForest,allround,31.32,2.24,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,heart,270,13,2,178,92,rotationForest,allround,21.09,1.31,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,hepatitis,80,19,2,52,28,rotationForest,allround,33.81,4.51,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,housing,506,13,3,333,173,rotationForest,allround,21.33,1,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,ionosphere,351,34,2,231,120,rotationForest,allround,5.53,0.72,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,iris,150,4,3,99,51,rotationForest,allround,4.37,1,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,liver,345,6,2,227,118,rotationForest,allround,29.68,1.99,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,lymphoma,148,18,4,97,51,rotationForest,allround,34.32,11.3,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pendigits,10992,16,10,7254,3738,rotationForest,allround,0.65,0.04,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,pima,768,8,2,506,262,rotationForest,allround,25.31,1.14,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,segment,2310,19,7,1524,786,rotationForest,allround,2.03,0.25,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,sonar,208,60,2,137,71,rotationForest,allround,17.26,2.24,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,soybean,683,35,19,450,233,rotationForest,allround,6.99,0.65,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spect,267,4,2,176,91,rotationForest,allround,17.71,1.18,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,spectf heart,269,44,2,177,92,rotationForest,allround,19.03,1.49,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vehicle,846,18,4,558,288,rotationForest,allround,21.82,1,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,votes,435,16,2,287,148,rotationForest,allround,4.22,0.72,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,vowel,990,11,11,653,337,rotationForest,allround,6.48,0.83,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wdbc,569,30,2,375,194,rotationForest,allround,4.93,0.64,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wine,178,13,3,117,61,rotationForest,allround,5.11,1.77,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,wpbc,194,32,2,128,66,rotationForest,allround,27.91,2.49,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,yeast,1484,8,10,979,505,rotationForest,allround,40.62,0.93,default,100,NA,20 runs 3-cv,paired t-test
RotBoost: A technique for combining Rotation Forest and AdaBoost,Zhang and Zhang,2008,Pattern Recognition Letters,zoo,101,16,7,66,35,rotationForest,allround,11.43,3.07,default,100,NA,20 runs 3-cv,paired t-test