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A new multi-objective binary Harris Hawks optimization for gene selection in microarray data by Ali Dabba; Abdelkamel Tari; Samy Meftali is a Computer Science article available to read on EtoBox.

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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 fi

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Author

Ali Dabba; Abdelkamel Tari; Samy Meftali

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)