Title: A hybrid feature selection method combining Gini index and support vector machine with recursive feature elimination for gene expression classification

Authors: Talal Almutiri; Faisal Saeed

Addresses: College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia ' College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

Abstract: Microarray datasets are suffering from a curse of dimensionality, because of a large number of genes and low numbers of samples, wherefore, the high dimensionality leads to computational cost and complexity. Consequently, feature selection (FS) is the process of choosing informative genes that could help in improving the effectiveness of classification. In this study, a hybrid feature selection was proposed, which combines the Gini index and support vector machine with recursive feature elimination (GI-SVM-RFE), calculates a weight for each gene and recursively selects only ten genes to be the informative genes. To measure the impact of the proposed method, the experiments include four scenarios: baseline without feature selection, GI feature selection, SVM-RFE feature selection, and combining GI with SVM-RFE. In this paper, 11 microarray datasets were used. The proposed method showed an improvement in terms of classification accuracy when compared with other previous studies.

Keywords: classification; feature selection; gene expression; Gini index; microarray; recursive feature elimination.

DOI: 10.1504/IJDMMM.2022.122038

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.1, pp.41 - 62

Received: 05 Dec 2019
Accepted: 26 Jun 2020
Published online: 08 Apr 2022 *

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