MRMR BA: A hybrid gene selection algorithm for cancer classification

The microarray technology facilitates biologist in monitoring the activity of thousands of genes (features) in one experiment. This technology generates gene expression data, which are significantly applicable for cancer classification. However, gene expression data consider as high- dimensional data which consists of irrelevant, redundant, and noisy genes that are unnecessary from the classification point of view. Recently, researchers have tried to figure out the most informative genes that contribute to cancer classification using computational intelligence algorithms. In this paper, we propose a filter method (Minimum Redundancy Maximum Relevancy, MRMR) and a wrapper method (Bat algorithm, BA) for gene selection in microarray dataset. MRMR was used to find the most important genes from all genes in gene expression data, and BA was employed to find the most informative gene subset from the reduce set generated by MRMR that can contribute in identifying the cancers. The wrapper method using support vector machine (SVM) method with 10-fold cross-validation served as evaluator of the BA. In order to test the accuracy performance of the proposed method, extensive experiments were conducted. Three microarray datasets are used, which include: colon, Breast, and Ovarian. Same method procedure was performed to Genetic algorithm (GA) to conducts comparison with our proposed method (MRMR-BA). The results show that our proposed method is able to find the smallest gene subset with highest classification accuracy.