Skip to main content

Table 4 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 GLIOMA dataset.

From: A stable gene selection in microarray data analysis

GLIOMA 5-Fold KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.660 ± 0.141 0.670 ± 0.140 0.671 ± 0.140 0.676/90 0.651 ± 0.134 0.679 ± 0.133 0.699 ± 0.131 0.701/97
GS1 0.674 ± 0.143 0.677 ± 0.148 0.679 ± 0.141 0.684/97 0.659 ± 0.145 0.698 ± 0.137 0.720 ± 0.138 0.722/99
Cho's 0.659 ± 0.145 0.660 ± 0.141 0.652 ± 0.131 0.664/31 0.618 ± 0.141 0.662 ± 0.131 0.668 ± 0.132 0.670/96
F-test 0.647 ± 0.140 0.663 ± 0.142 0.667 ± 0.133 0.674/91 0.639 ± 0.138 0.672 ± 0.131 0.684 ± 0.130 0.685/84
GLIOMA LOO KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/# Genes
GS2 0.760 0.700 0.660 0.780/28 0.700 0.660 0.760 0.760/96
GS1 0.700 0.760 0.740 0.780/35 0.680 0.700 0.760 0.760/45
Cho's 0.720 0.640 0.640 0.820/20 0.640 0.680 0.620 0.720/2
F-test 0.700 0.660 0.700 0.780/70 0.640 0.620 0.740 0.740/100