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Table 5 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 LUNG dataset.

From: A stable gene selection in microarray data analysis

LUNG 5-Fold KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/#Genes
GS2 0.884 ± 0.053 0.916 ± 0.041 0.928 ± 0.037 0.928/100 0.858 ± 0.061 0.913 ± 0.035 0.931 ± 0.033 0.931/99
GS1 0.890 ± 0.046 0.919 ± 0.041 0.937 ± 0.034 0.937/99 0.871 ± 0.051 0.922 ± 0.038 0.938 ± 0.031 0.938/98
Cho's 0.843 ± 0.053 0.897 ± 0.044 0.924 ± 0.038 0.924/100 0.803 ± 0.065 0.894 ± 0.044 0.924 ± 0.035 0.924/100
F-test 0.873 ± 0.049 0.882 ± 0.044 0.918 ± 0.044 0.918/100 0.852 ± 0.055 0.901 ± 0.042 0.930 ± 0.036 0.930/100
LUNG LOO KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.892 0.906 0.921 0.931/44 0.867 0.892 0.931 0.931/73
GS1 0.887 0.941 0.941 0.951/49 0.862 0.941 0.941 0.951/51
Cho's 0.837 0.897 0.921 0.926/86 0.773 0.892 0.931 0.941/88
F-test 0.872 0.877 0.901 0.921/89 0.857 0.901 0.926 0.936/94