Feature Selection for Cancer Classification Based on Support Vector Machine

Feature selection plays an important role in cancer classification, for gene expression data usually have a large number of dimensions and relatively a small number of samples In this paper we use the support vector machine (SVM) for cancer classification. We propose a mixed two-step feature selection method The first step uses a modified t-test method to select discriminatory features The second step extracts principal components from the top-ranked genes based on the modified t-test method We tested our two-step method in three data sets, i e, the lymphoma data set, the SRBCT data set, and the ovarian cancer data set. The results in all the three data sets show our two-step methods is able to achieve 100% accuracy with much fewer genes than other published results