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Table 6 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 DLBCL dataset.

From: A stable gene selection in microarray data analysis

DLBCL 5-Fold KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.881 ± 0.081 0.906 ± 0.078 0.914 ± 0.074 0.916/98 0.872 ± 0.081 0.918 ± 0.068 0.933 ± 0.054 0.933/98
GS1 0.878 ± 0.078 0.895 ± 0.075 0.903 ± 0.076 0.903/100 0.861 ± 0.079 0.895 ± 0.075 0.917 ± 0.066 0.918/98
Cho's 0.874 ± 0.085 0.909 ± 0.075 0.920 ± 0.072 0.920/99 0.869 ± 0.085 0.915 ± 0.068 0.930 ± 0.061 0.930/99
F-test 0.877 ± 0.079 0.893 ± 0.078 0.902 ± 0.080 0.902/100 0.869 ± 0.079 0.910 ± 0.074 0.925 ± 0.063 0.926/95
DLBCL LOO KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.883 0.922 0.922 0.935/70 0.896 0.961 0.948 0.961/55
GS1 0.896 0.896 0.922 0.922/85 0.870 0.883 0.961 0.961/81
Cho's 0.883 0.922 0.922 0.935/69 0.909 0.896 0.948 0.961/74
F-test 0.896 0.896 0.883 0.922/61 0.857 0.935 0.948 0.961/92