Binary JAYA Algorithm with Adaptive Mutation for Feature Selection

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dc.authoridhttps://orcid.org/0000-0002-7815-8946en_US
dc.authoridhttps://orcid.org/0000-0003-1980-1791en_US
dc.authoridhttps://orcid.org/0000-0002-1135-5750en_US
dc.contributor.authorAwadallah, Mohammed A.
dc.contributor.authorAl-Betar, Mohammed Azmi
dc.contributor.authorHammouri, Abdelaziz I.
dc.contributor.authorAlomari, Osama Ahmad
dc.date.accessioned2023-09-22T06:50:02Z
dc.date.available2023-09-22T06:50:02Z
dc.date.issued2020en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractIn this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate (Rm) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results.en_US
dc.identifier.doi10.1007/s13369-020-04871-2en_US
dc.identifier.endpage10890en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85090125600en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10875en_US
dc.identifier.urihttps://hdl.handle.net/11363/5620
dc.identifier.urihttps://doi.org/
dc.identifier.volume45en_US
dc.identifier.wosWOS:000565112700005en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlomari, Osama Ahmad
dc.language.isoenen_US
dc.publisherSPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYen_US
dc.relation.ispartofArabian Journal for Science and Engineeringen_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.subjectJAYA algorithmen_US
dc.subjectFeature selectionen_US
dc.subjectMachine learningen_US
dc.subjectMetaheuristicen_US
dc.subjectOptimizationen_US
dc.titleBinary JAYA Algorithm with Adaptive Mutation for Feature Selectionen_US
dc.typeArticleen_US

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