Gene Selection for Cancer Classification using a New Hybrid of Binary Black Hole Algorithm

This paper proposes a new hybrid approach for solving gene selection problems in cancer microarray data, which is one of the most challenging tasks in bioinformatics. Minimum-redundancy-maximum-relevance (mRMR) filter approach is combined with the binary black hole optimization algorithm (BBHA) to pick out extremely discriminative genes from cancer datasets. The support vector machine (SVM) is employed as a fitness function to accurately diagnose cancer. The experimental results prove that the suggested method exhibits better classification accuracy with the smallest gene subset compared to existing state-of-art methods.