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dc.contributor.authorRosales Pérez, Alejandro
dc.contributor.authorGarcía López, Salvador 
dc.contributor.authorHerrera Triguero, Francisco 
dc.date.accessioned2022-05-04T11:41:55Z
dc.date.available2022-05-04T11:41:55Z
dc.date.issued2022-04-21
dc.identifier.citationPublished version: A. Rosales-Pérez, S. García and F. Herrera, "Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization," in IEEE Transactions on Cybernetics, doi: [10.1109/TCYB.2022.3163974]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/74696
dc.descriptionThis work was partially supported by Project PID2020-119478GBI00 funded by MICINN/AEI/10.13039/501100011033 and by Project A-TIC-434-UGR20 funded by FEDER/Junta de Andaluc´ıa- Consejer´ıa de Transformaci´on Econ´omica, Industria, Conocimiento y Universidades. The authors acknowledge the support from “Laboratorio de Superc´omputo del Baj´ıo” through project 300832 from CONACyT.es_ES
dc.description.abstractSupport vector machines are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This paper introduces EBCS-SVM: Evolutionary Bilevel Costsensitive Support Vector Machines. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyper-parameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower-level determines the support vectors and the upper-level the hyper-parameters. This optimization problem is solved using an evolutionary algorithm at the upper-level and Sequential Minimal Optimization at the lower-level. These two methods work in a nested fashion, i.e., the optimal support vectors help guide the search of the hyper-parameters, and the lower-level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets.es_ES
dc.description.sponsorshipPID2020-119478GBI00 funded by MICINN/AEI/10.13039/501100011033es_ES
dc.description.sponsorshipA-TIC-434-UGR20 funded by FEDER/Junta de Andaluc´ıa- Consejer´ıa de Transformaci´on Econ´omica, Industria, Conocimiento y Universidadeses_ES
dc.description.sponsorship300832 from CONACyTes_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectSupport Vector Machineses_ES
dc.subjectImbalanced classificationes_ES
dc.subjectData Preprocessinges_ES
dc.subjectEvolutionary algorithmses_ES
dc.subjectBilevel optimizationes_ES
dc.titleHandling Imbalanced Classification Problems with Support Vector Machines via Evolutionary Bilevel Optimizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España