Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high‑dimensional biomedical data

Abstract

Gene expression data play a signifcant role in the development of efective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively afect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid flter/wrapper gene selection method, called mRMR-BAOACSA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a frst-stage flter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classifcation purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classifcation accuracy and the minimum number of selected genes.