Title: Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm
Authors: Osama Ahmad Alomari; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Laith Mohammad Abualigah
Addresses: School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia ' School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia ' Department of Information Technology, Al-Balqa Applied University, Al-Huson University College, Al-Huson, Irbid, Jordan ' School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia
Abstract: In this paper, the bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets. Microarray data consists of irrelevant, redundant, and noisy genes. Gene selection problem is tackled by determining the most informative genes taken from microarray data to accurately diagnose the cancer disease. Gene selection problem is widely solved by optimisation algorithms. BA is a recent swarm-based algorithm, which imitates the echolocation system of bat individuals. It has been successfully applied to several optimisation problems. Gene selection is tackled by combining two stages, namely, filter stage, which uses Minimum Redundancy Maximum Relevancy (MRMR) method; and wrapper stage, which uses BA and SVM. To test the accuracy performance of the proposed method, ten microarray datasets were used. For comparative evaluation, the proposed method was compared with popular gene selection methods. The proposed method achieves comparable results of some datasets and produced new results for one dataset.
Keywords: bat-inspired algorithm; optimisation; gene selection; MRMR; SVM; classification; computational biology; data mining; gene expression; DNA microarrays.
International Journal of Data Mining and Bioinformatics, 2017 Vol.19 No.1, pp.32 - 51
Received: 29 Dec 2016
Accepted: 26 Jul 2017
Published online: 02 Dec 2017 *