Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets

Abstract

Microarray cancer gene expression datasets are high dimensional and complex for efficient computational analysis. Thus, selecting high discriminative genes from microarray data has become increasingly interesting in the field of bioinformatics. This article addresses the problem of designing a gene selection algorithm which is modeled as a multiobjective optimization problem with the sensitivity, specificity and the number of selected genes. Then, a multi-objective ranking binary artificial bee colony algorithm based on decomposition is proposed to select the optimal subset of dimensions from the original high dimensional data while retaining a subset that satisfies the defined objective. First, the Fisher-Markov selector is used to choose a fixed number of microarray data. Second, to make artificial bee colony algorithm suitable for the binary problem, a novel binary update strategy is proposed to balance the exploration and exploitation ability. Third, multi-objective ranking binary artificial bee colony algorithm is proposed by integrating tchebycheff approach into the ranking binary artificial bee colony algorithm. Finally, the multi-objective ranking binary artificial bee colony algorithm based on decomposition (MORBABC/D) method is used for feature selection, and extreme learning machine is used as the classifier with 10 fold cross-validation method. In order to show the effectiveness and efficiency of the algorithm, the proposed algorithm is tested on eight microarray dataset. Experimental studies have been carried out to investigate the ability of the proposed algorithm.