For the breast cancer dataset, the significance level was 0.001, and the average classification accuracy rate of the K-S test was slightly worse than that of the Wilcoxon test; however it was better than that of the T test.
Based on the above results, the K-S test was superior to the Wilcoxon test and the T test for gene selection.
First, all of the genes were prescreened by the K-S test with a significance level of 0.01, and a preselected gene subset was obtained.
Comparison of the K-S Test-CF Algorithm with the K-S Test, CFS, mRMR, and ReliefF Algorithms.
(i) The K-S test-CFS algorithm achieved a better performance than the other gene selection algorithms on almost all datasets.
However, its performance was not always as good as that of the K-S test-CFS algorithm.
In summary, the performance of the K-S test-CFS is superior to other gene filtering algorithms.
In this paper, we present a K-S test-CFS selection algorithm developed by combining the K-S test and CFS.