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dc.contributor.authorToo, Jingwei
dc.contributor.authorSadiq, Ali Safaa
dc.contributor.authorMirjalili, Seyed Mohammad
dc.date.accessioned2021-11-03T10:56:34Z
dc.date.available2021-11-03T10:56:34Z
dc.date.issued2021-11-22
dc.identifier.citationToo, J., Sadiq, A. S. & Mirjalili, S. M. (2021) A conditional opposition-based particle swarm optimisation for feature selection, Connection Science, DOI: 10.1080/09540091.2021.2002266en
dc.identifier.issn0954-0091
dc.identifier.doi10.1080/09540091.2021.2002266
dc.identifier.urihttp://hdl.handle.net/2436/624432
dc.descriptionThis is an accepted manuscript of an article published by Taylor & Francis on 22/11/2021. Available online: https://doi.org/10.1080/09540091.2021.2002266 The accepted version of the publication may differ from the final published version.en
dc.description.abstractBecause of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimization has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimization tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimization. In this paper, a conditional opposition-based particle swarm optimization is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimization. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/09540091.2021.2002266en
dc.subjectfeature selectionen
dc.subjectparticle swarm optimizationen
dc.subjectwrapper approachen
dc.subjectclassificationen
dc.subjectdata miningen
dc.titleA conditional opposition-based particle swarm optimization for feature selectionen
dc.typeJournal articleen
dc.identifier.eissn1360-0494
dc.identifier.journalConnection Scienceen
dc.date.updated2021-11-02T21:54:17Z
dc.date.accepted2021-10-29
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW03112021ASen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2022-12-31en
refterms.dateFCD2021-11-03T10:55:14Z
refterms.versionFCDVoR
refterms.dateFOA2021-12-22T16:20:37Z


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