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Table 3 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 LEU dataset.

From: A stable gene selection in microarray data analysis

LEU 5-Fold KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/#Genes
GS2 0.961 ± 0.048 0.968 ± 0.044 0.971 ± 0.040 0.971/85 0.958 ± 0.052 0.967 ± 0.047 0.974 ± 0.039 0.974/98
GS1 0.965 ± 0.048 0.973 ± 0.040 0.979 ± 0.034 0.979/100 0.965 ± 0.050 0.970 ± 0.043 0.979 ± 0.037 0.979/93
Cho's 0.958 ± 0.049 0.963 ± 0.046 0.968 ± 0.043 0.968/100 0.953 ± 0.054 0.962 ± 0.053 0.970 ± 0.043 0.970/98
F-test 0.960 ± 0.049 0.966 ± 0.045 0.974 ± 0.038 0.974/96 0.957 ± 0.055 0.968 ± 0.049 0.975 ± 0.039 0.975/99
LEU LOO KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/# Genes
GS2 0.944 0.972 0.958 0.986/10 0.958 0.958 0.972 0.986/25
GS1 0.958 0.986 0.972 0.986/60 0.972 0.986 0.986 0.986/4
Cho's 0.944 0.944 0.958 0.972/9 0.958 0.958 0.986 0.986/80
F-test 0.944 0.944 0.972 0.986/25 0.958 0.958 0.972 0.986/33