A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm with β-hill climbing

This paper proposed a new gene selection method based on modified Minimum Redundancy Maximum Relevancy (MRMR) as a filtering approach and hybrid bat algorithm with β-hill climbing as an efficient wrapper approach. The gene selection is a process of selecting the discriminative genes that aid in the development of efficient cancer diagnosis and classification. In general, the current filter-based approaches produced gene subset according to its discriminative power. However, one of the deficiencies of single filter approaches is that it has high variability of the classification results. Accordingly, this study aim to improve MRMR through incorporating its with ensemble of filters to increase the robustness and the stability of MRMR. The result of filtering-based approach is a set of discriminative genes. The wrapper-based approach considers the results from the filtering-based approach to formulate the gene selection search space. In wrapper approach, bat algorithm is tailored for gene selection problem and hybridized with a powerful local search method called beta hill climbing to further stress the deep learning side in the search space navigation and thus find a very robust and stable discriminative genes. Bat-inspired algorithm (BA) is a recent swarm-based optimization method while β-hill climbing is an exploratory local search. The proposed method is called Robust MRMR and Hybrid Bat-inspired Algorithm (rMRMR-HBA). To evaluate the proposed method, ten well-known microarray datasets are experimented with. These datasets are varies in terms of number of genes, samples, and classes. For performance evaluation, the proposed filtering-based approach (i.e., rMRMR) is initially tested against the standard MRMR and other well-regard filtering approaches. Thereafter, the wrapper-based approach (i.e., HBA) is evaluated by studying the convergence behavior of BA with and without β-hill climbing. For comparative evaluation, the results of the proposed rMRMR-HBA were compared with state-of-art methods using the same microarray datasets. The comparative results show that our proposed approach achieved outstanding results in two out of ten datasets in terms of clarification accuracy and minimum number of genes.

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