Boosted sooty tern optimization algorithm for global optimization and feature selection

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dc.authorscopusid43361385400
dc.authorscopusid55391699600
dc.authorscopusid55354654200
dc.authorscopusid57219716093
dc.authorscopusid57195676453
dc.contributor.authorHoussein, Essam H.
dc.contributor.authorOliva, D.
dc.contributor.authorÇelik, Emre
dc.contributor.authorEmam, M.M.
dc.contributor.authorGhoniem, R.M.
dc.date.accessioned2023-07-26T11:53:53Z
dc.date.available2023-07-26T11:53:53Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractFeature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. © 2022 Elsevier Ltden_US
dc.identifier.doi10.1016/j.eswa.2022.119015
dc.identifier.issn0957-4174
dc.identifier.scopus2-s2.0-85140292803en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.119015
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12646
dc.identifier.volume213en_US
dc.identifier.wosWOS:000877842900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇelik, Emre
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectDimensionality reductionen_US
dc.subjectFeature selectionen_US
dc.subjectMetaheuristicsen_US
dc.subjectOptimization algorithmen_US
dc.subjectSooty Tern Optimization Algorithm (STOA)en_US
dc.subjectClassification (of information)en_US
dc.subjectData miningen_US
dc.subjectGlobal optimizationen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectDimensionality reductionen_US
dc.subjectFeature selection problemen_US
dc.subjectFeatures selectionen_US
dc.subjectGlobal featureen_US
dc.subjectGlobal optimisationen_US
dc.subjectMetaheuristicen_US
dc.subjectOptimization algorithmsen_US
dc.subjectOptimization problemsen_US
dc.subjectSooty tern optimization algorithmen_US
dc.subjectFeature Selectionen_US
dc.titleBoosted sooty tern optimization algorithm for global optimization and feature selectionen_US
dc.typeArticleen_US

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