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Table 7 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 MLL dataset.

From: A stable gene selection in microarray data analysis

MLL 5-Fold KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/#Genes
GS2 0.937 ± 0.056 0.947 ± 0.055 0.948 ± 0.053 0.949/91 0.926 ± 0.058 0.941 ± 0.052 0.947 ± 0.051 0.947/87
GS1 0.946 ± 0.054 0.940 ± 0.057 0.942 ± 0.058 0.948/29 0.932 ± 0.059 0.947 ± 0.053 0.952 ± 0.050 0.952/99
Cho's 0.950 ± 0.048 0.954 ± 0.048 0.960 ± 0.045 0.960/93 0.942 ± 0.051 0.946 ± 0.050 0.955 ± 0.048 0.955/89
F-test 0.949 ± 0.050 0.950 ± 0.050 0.953 ± 0.051 0.954/99 0.943 ± 0.051 0.945 ± 0.053 0.948 ± 0.051 0.948/100
MLL LOO KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.944 0.958 0.972 0.972/90 0.917 0.958 0.944 0.972/91
GS1 0.958 0.944 0.958 0.972/97 0.958 0.958 0.958 0.972/56
Cho's 0.944 0.944 0.958 0.972/23 0.944 0.931 0.944 0.958/44
F-test 0.944 0.944 0.958 0.958/65 0.944 0.931 0.944 0.958/31