Citation

Mohd Ali, Nursabillilah and Besar, Rosli and Ab Aziz, Nor Azlina (2022) Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review. Symmetry, 14 (10). p. 1955. ISSN 2073-8994

Text 29.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer.

Item Type:	Article
Uncontrolled Keywords:	Microarray breast cancer, microarray cancer, metaheuristic method, hybrid feature selection
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Jan 2023 02:07
Last Modified:	06 Jan 2023 02:07
URII:	http://shdl.mmu.edu.my/id/eprint/10832

Downloads

Edit (login required)