Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high‑dimensional biomedical data

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dc.authoridhttps://orcid.org/0000-0001-7401-4964en_US
dc.authoridhttps://orcid.org/0000-0001-9391-9785en_US
dc.contributor.authorPashaei, Elham
dc.contributor.authorPashaei, Elnaz
dc.date.accessioned2023-10-21T09:02:58Z
dc.date.available2023-10-21T09:02:58Z
dc.date.issued2022en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractGene expression data play a signifcant role in the development of efective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively afect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid flter/wrapper gene selection method, called mRMR-BAOACSA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a frst-stage flter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classifcation purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classifcation accuracy and the minimum number of selected genes.en_US
dc.identifier.doi10.1007/s11227-022-04507-2en_US
dc.identifier.endpage15637en_US
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85128469275en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage15598en_US
dc.identifier.urihttps://hdl.handle.net/11363/6001
dc.identifier.urihttps://doi.org/
dc.identifier.volume78en_US
dc.identifier.wosWOS:000784921200002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPashaei, Elham
dc.language.isoenen_US
dc.publisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDSen_US
dc.relation.ispartofThe Journal of Supercomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectCancer classifcationen_US
dc.subjectFeature selectionen_US
dc.subjectArithmetic optimization algorithmen_US
dc.subjectGene selectionen_US
dc.subjectOptimizationen_US
dc.titleHybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high‑dimensional biomedical dataen_US
dc.typeArticleen_US

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