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dc.contributor.authorZhang, Zhongliang
dc.contributor.authorKrawczyk, Bartosz
dc.contributor.authorGarcía López, Salvador 
dc.contributor.authorRosales-Pérez, Alejandro
dc.contributor.authorHerrera Triguero, Francisco 
dc.date.accessioned2020-12-21T12:51:36Z
dc.date.available2020-12-21T12:51:36Z
dc.date.issued2016-04-01
dc.identifier.citationPublished version: Zhang, Z., Krawczyk, B., Garcìa, S., Rosales-Pérez, A., & Herrera, F. (2016). Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data. Knowledge-Based Systems, 106, 251-263. [https://doi.org/10.1016/j.knosys.2016.05.048]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/65075
dc.descriptionZhongliang Zhang was supported by the National Science Foundation of China (NSFC Proj. 61273204) and CSC Scholarship Program (CSC NO. 201406080059). Bartosz Krawczyk was supported by the Polish National Science Center under the grant no. UMO-2015/19/B/ST6/01597. Salvador Garcia and Francisco Herrera were partially supported by the Spanish Ministry of Education and Science under Project TIN2014-57251-P and the Andalusian Research Plan P10-TIC-6858, P11-TIC-7765. Alejandro Rosales-Perez was supported by the CONACyT grant 329013.es_ES
dc.description.abstractMulti-class imbalance classification problems occur in many real-world applications, which suffer from the quite different distribution of classes. Decomposition strategies are well-known techniques to address the classification problems involving multiple classes. Among them binary approaches using one-vs-one and one-vs-all has gained a significant attention from the research community. They allow to divide multi-class problems into several easier-to-solve two-class sub-problems. In this study we develop an exhaustive empirical analysis to explore the possibility of empowering the one-vs-one scheme for multi-class imbalance classification problems with applying binary ensemble learning approaches. We examine several state-of-the-art ensemble learning methods proposed for addressing the imbalance problems to solve the pairwise tasks derived from the multi-class data set. Then the aggregation strategy is employed to combine the binary ensemble outputs to reconstruct the original multi-class task. We present a detailed experimental study of the proposed approach, supported by the statistical analysis. The results indicate the high effectiveness of ensemble learning with one-vs-one scheme in dealing with the multi-class imbalance classification problems.es_ES
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC) 61273204es_ES
dc.description.sponsorshipCSC Scholarship Program (CSC) 201406080059es_ES
dc.description.sponsorshipPolish National Science Center UMO-2015/19/B/ST6/01597es_ES
dc.description.sponsorshipSpanish Government TIN2014-57251-Pes_ES
dc.description.sponsorshipAndalusian Research Plan P10-TIC-6858 P11-TIC-7765es_ES
dc.description.sponsorshipConsejo Nacional de Ciencia y Tecnologia (CONACyT) 329013es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectMulti-class classificationes_ES
dc.subjectImbalanced dataes_ES
dc.subjectEnsemble learninges_ES
dc.subjectBinary decompositiones_ES
dc.subjectClassifier combinationes_ES
dc.titleEmpowering One-vs-One Decomposition with Ensemble Learning for Multi-Class Imbalanced Dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.knosys.2016.05.048
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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