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Table 2 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 CAR dataset.

From: A stable gene selection in microarray data analysis

CAR 5-Fold KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.578 ± 0.118 0.810 ± 0.084 0.865 ± 0.059 0.865/100 0.528 ± 0.116 0.812 ± 0.080 0.870 ± 0.053 0.870/100
GS1 0.634 ± 0.136 0.831 ± 0.079 0.874 ± 0.058 0.874/100 0.600 ± 0.140 0.824 ± 0.076 0.885 ± 0.050 0.885/100
Cho's 0.471 ± 0.091 0.676 ± 0.083 0.797 ± 0.070 0.797/100 0.437 ± 0.089 0.651 ± 0.085 0.821 ± 0.066 0.821/100
F-test 0.681 ± 0.091 0.788 ± 0.071 0.851 ± 0.065 0.851/100 0.649 ± 0.093 0.802 ± 0.071 0.868 ± 0.056 0.868/100
CAR LOO KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/# Genes
GS2 0.621 0.828 0.885 0.885/99 0.557 0.822 0.868 0.874/71
GS1 0.718 0.822 0.868 0.879/97 0.695 0.828 0.902 0.902/100
Cho's 0.448 0.661 0.787 0.805/88 0.466 0.661 0.851 0.879/97
F-test 0.707 0.776 0.856 0.862/85 0.626 0.793 0.874 0.885/97