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Table 1 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 SRBCT dataset. Listed are the accuracies when the numbers of selected genes are 30, 60, and 100, respectively, together with their standard deviations for 5-fold cross validation, and the best accuracy together with the number of selected genes.

From: A stable gene selection in microarray data analysis

SRBCT 5-Fold KNN SVMs
  30 60 100 Best Accuracy/# Genes 30 60 100 Best Accuracy/# Genes
GS2 0.953 ± 0.048 0.971 ± 0.041 0.980 ± 0.038 0.981/90 0.949 ± 0.047 0.976 ± 0.040 0.990 ± 0.026 0.990/99
GS1 0.941 ± 0.047 0.961 ± 0.045 0.977 ± 0.041 0.980/88 0.959 ± 0.054 0.978 ± 0.040 0.988 ± 0.030 0.979/93
Cho's 0.820 ± 0.096 0.864 ± 0.093 0.896 ± 0.087 0.902/98 0.835 ± 0.088 0.918 ± 0.069 0.943 ± 0.062 0.943/98
F-test 0.963 ± 0.050 0.973 ± 0.046 0.978 ± 0.040 0.980/90 0.970 ± 0.042 0.980 ± 0.039 0.992 ± 0.021 0.992/95
SRBCT LOO KNN SVMs
  30 60 100 Best Accuracy/#Genes 30 60 100 Best Accuracy/# Genes
GS2 0.964 0.976 0.964 0.988/77 0.952 0.976 1.000 1.000/96
GS1 0.964 0.988 0.988 0.988/57 0.976 0.988 0.988 0.988/34
Cho's 0.831 0.880 0.892 0.928/82 0.819 0.928 0.964 0.988/80
F-test 0.976 0.976 0.988 0.988/89 0.976 0.980 0.988 1.000/78