Hybrid DE-SVM Approach for Feature Selection: Application to Gene Expression Datasets

Abstract

The efficient selection of predictive and accurate gene subsets for cell-type classification is nowadays a crucial problem in Microarray data analysis. The application and combination of dedicated computational intelligence methods holds a great promise for tackling the feature selection and classification. In this work we present a Differential Evolution (DE) approach for the efficient automated gene subset selection. In this model, the selected subsets are evaluated by means of their classification rate using a Support Vector Machines (SVM) classifier. The proposed approach is tested on DLBCL Lymphoma and Colon Tumor gene expression datasets. Experiments lying in effectiveness and biological analyses of the results, in addition to comparisons with related methods in the literature, indicate that our DE-SVM model is highly reliable and competitive.