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An Effective Metaheuristic for Bi-objective Feature Selection in Two-Class Classification Problem

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dc.contributor.authorNúñez Letamendía, Laura
dc.contributor.authorLyubchenko, Alexander
dc.contributor.authorPacheco, Joaquín Antonio
dc.contributor.authorCasado Yusta, Silvia
dc.contributor.rorhttps://ror.org/02jjdwm75
dc.date.accessioned2025-04-08T10:34:10Z
dc.date.available2025-04-08T10:34:10Z
dc.date.issued2019
dc.description.abstractFeature selection is known as a very useful technique in machine learning practice as it may result in the development of more straightforward models with better accuracy. Traditionally, feature selection is considered as a single-objective problem, however, it can be easily formulated in terms of two objectives. The solving of such problems requires the application of appropriate multi-objective optimization methods that do not always offer equally good solutions even under the same conditions. This paper focuses on the development of a metaheuristic optimization approach for bi-objective feature selection problem in two-class classification. We consider the solving of this problem in terms of minimization of both misclassification error and feature subset size. For solving the considered problem, an adaptation of the Multi-Objective Adaptive Memory Programming (MOAMP) metaheuristic based on the tabu search strategy is proposed. Our MOAMP adaption has been utilized to obtain the sets of most relevant features for two real classification problems with two classes. Finally, using popular Pareto front quality indicators, the obtained results have been compared with the sets of non-dominated solutions derived by the well-known NSGA2 algorithm. The conducted research allows concluding about the ability of the MOAMP adaptation to get a better efficient frontier for the same number of objective function calls.
dc.description.peerreviewedyes
dc.description.statusPublished
dc.formatapplication/pdf
dc.identifier.citationLyubchenko, A. A., Pacheco, J. A., Casado, S., & Nuñez, L. (2019, March). An effective metaheuristic for bi-objective feature selection in two-class classification problem. In Journal of Physics: Conference Series (Vol. 1210, No. 1, p. 012086). IOP Publishing. https://doi.org/10.1088/1742-6596/1210/1/012086
dc.identifier.doihttps://doi.org/10.1088/1742-6596/1210/1/012086
dc.identifier.issn1742-6596
dc.identifier.urihttps://hdl.handle.net/20.500.14417/3730
dc.issue.number1
dc.journal.titleJournal of Physics: Conference SeriesJournal of Physics: Conference Series issn
dc.language.isoen
dc.page.total9
dc.publisherIOP Publishing Ltd
dc.relation.departmentFinance
dc.relation.entityIE University
dc.relation.schoolIE Business School
dc.rightsAttribution 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed
dc.subject.keywordBi-objective feature selection
dc.subject.keywordClassification
dc.subject.keywordTabu search
dc.subject.keywordMOAMP
dc.titleAn Effective Metaheuristic for Bi-objective Feature Selection in Two-Class Classification Problem
dc.typeinfo:eu-repo/semantics/article
dc.version.typeinfo:eu-repo/semantics/publishedVersion
dc.volume.number1210
dspace.entity.typePublication
relation.isAuthorOfPublicationfad2f1f5-aa2e-4324-a49b-81ee05aea70f
relation.isAuthorOfPublication.latestForDiscoveryfad2f1f5-aa2e-4324-a49b-81ee05aea70f
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