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Table 8 The leave-one-out and 5-fold cross validation classification accuracies of the SVM-classifier and the KNN-classifier based on four gene selection methods, GS1, GS2, Cho's, and F-test, on the PROSTATE dataset.

From: A stable gene selection in microarray data analysis

PROSTATE 5-Fold

KNN

SVMs

 

30

60

100

Best Accuracy/# Genes

30

60

100

Best Accuracy/#Genes

GS2

0.917 ± 0.073

0.916 ± 0.056

0.913 ± 0.057

0.921/39

0.884 ± 0.080

0.908 ± 0.057

0.909 ± 0.060

0.911/91

GS1

0.918 ± 0.073

0.917 ± 0.056

0.907 ± 0.062

0.922/35

0.887 ± 0.082

0.901 ± 0.060

0.914 ± 0.058

0.914/99

Cho's

0.870 ± 0.144

0.918 ± 0.055

0.914 ± 0.058

0.918/10

0.841 ± 0.149

0.890 ± 0.069

0.904 ± 0.061

0.904/4

F-test

0.921 ± 0.053

0.915 ± 0.056

0.913 ± 0.057

0.935/61

0.893 ± 0.060

0.907 ± 0.062

0.914 ± 0.058

0.918/92

PROSTATE LOO

KNN

SVMs

 

30

60

100

Best Accuracy/# Genes

30

60

100

Best Accuracy/#Genes

GS2

0.931

0.922

0.922

0.941/8

0.902

0.902

0.941

0.951/47

GS1

0.931

0.922

0.902

0.951/8

0.931

0.912

0.922

0.951/49

Cho's

0.931

0.912

0.912

0.941/8

0.941

0.912

0.912

0.941/20

F-test

0.931

0.922

0.922

0.941/10

0.892

0.931

0.931

0.941/4