Selection and classification of gene expression data using a MF-GA-TS-SVM approach

Abstract

This article proposes a Multiple-Filter (MF) using a genetic algorithm (GA) and Tabu Search (TS) combined with a Support Vector Machine (SVM) for gene selection and classification of DNA microarray data. The proposed method is designed to select a subset of relevant genes that classify the DNA-microarray data more accurately. First, five traditional statistical methods are used for preliminary gene selection (Multiple Filter). Then different relevant gene subsets are selected by using a Wrapper (GA/TS/SVM). A gene subset, consisting of relevant genes, is obtained from each statistical method, by analyzing the frequency of each gene in the different gene subsets. Finally, the most frequent genes are evaluated by the Multiple Wrapper approach to obtain a final relevant gene subset. The proposed method is tested in four DNA-microarray datasets. In the experimental results it is observed that our model work very well than other methods reported in the literature. © 2014 Springer International Publishing Switzerland.