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A new multi-objective binary Harris Hawks optimization for gene selection in microarray data

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Abstract

Cancer classification is one of the main applications of gene expression data (microarray data) and is essential for a comprehensive diagnosis of cancer treatment. Therefore, bio-inspired algorithms have developed several effective applications in the analysis of gene selection, which are one of the most effective applied in this domain. Harris Hawks optimization is a novel and recent algorithm that has an excellent balance between exploration and exploitation. This paper presents the first study on multi-objective binary Harris Hawks optimization (MOBHHO) for gene selection. We define gene selection as a problem, including two main conflicting objectives: minimizing the number of genes and maximizing the classification accuracy. MOBHHO uses two fitness functions to solve competing objectives. The first function based on SVM with LOOCV classifier and the second function also depends on KNN with K-fold classifier, as well as the percentage of gene selection found in both functions. Furthermore, MOBHHO tries to find the Pareto-optimal solutions, i.e. the best gene subset that contains a minimal number of selected genes and better classification accuracy. We have integrated several filter-based ranking methods with our proposal. In order to test the performance accuracy of the proposed MOBHHO algorithm, we compared our algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on eight benchmarks (binary-class and multi-class), MOBHHO able to provide a minimum number of genes to obtain the highest classification accuracy. The proposed method reaches above 98% classification accuracy in six benchmark datasets and a maximum accuracy of 100% is achieved.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Mathematics and Computer Science, Mohamed Boudiaf University, M’sila, Algeria

    Ali Dabba

  2. Computer Science Department, Faculty of Sciences, Abderrahmane Mira University, Bejaïa, Algeria

    Ali Dabba & Abdelkamel Tari

  3. Medical Computing Laboratory-LIMED, Bejaïa, Algeria

    Abdelkamel Tari

  4. University of Lille, Lille, France

    Samy Meftali

  5. Research center in Computer Science, Signal and Automatic Control of Lille-CRIStAL, Lille, France

    Ali Dabba & Samy Meftali

  6. Laboratory of Informatics and its Applications of M’sila-LIAM, M’sila, Algeria

    Ali Dabba

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  1. Ali Dabba
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  2. Abdelkamel Tari
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Correspondence to Ali Dabba.

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Dabba, A., Tari, A. & Meftali, S. A new multi-objective binary Harris Hawks optimization for gene selection in microarray data. J Ambient Intell Human Comput 14, 3157–3176 (2023). https://doi.org/10.1007/s12652-021-03441-0

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