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dc.contributor.authorJuez Gil, Mario 
dc.contributor.authorArnaiz González, Álvar 
dc.contributor.authorRodríguez Diez, Juan José 
dc.contributor.authorGarcía Osorio, César 
dc.date.accessioned2021-05-14T11:34:01Z
dc.date.available2021-05-14T11:34:01Z
dc.date.issued2021-09
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/10259/5766
dc.description.abstractDatasets are growing in size and complexity at a pace never seen before, forming ever larger datasets known as Big Data. A common problem for classification, especially in Big Data, is that the numerous examples of the different classes might not be balanced. Some decades ago, imbalanced classification was therefore introduced, to correct the tendency of classifiers that show bias in favor of the majority class and that ignore the minority one. To date, although the number of imbalanced classification methods have increased, they continue to focus on normal-sized datasets and not on the new reality of Big Data. In this paper, in-depth experimentation with ensemble classifiers is conducted in the context of imbalanced Big Data classification, using two popular ensemble families (Bagging and Boosting) and different resampling methods. All the experimentation was launched in Spark clusters, comparing ensemble performance and execution times with statistical test results, including the newest ones based on the Bayesian approach. One very interesting conclusion from the study was that simpler methods applied to unbalanced datasets in the context of Big Data provided better results than complex methods. The additional complexity of some of the sophisticated methods, which appear necessary to process and to reduce imbalance in normal-sized datasets were not effective for imbalanced Big Data.en
dc.description.sponsorship“la Caixa” Foundation, Spain, under agreement LCF/PR/PR18/51130007. This work was supported by the Junta de Castilla y León, Spain under project BU055P20 (JCyL/FEDER, UE) co-financed through European Union FEDER funds, and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund, Spain through a pre-doctoral grant (EDU/1100/2017).es
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofApplied Soft Computing. 2021, V. 108, 107447es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectUnbalanceen
dc.subjectImbalanceen
dc.subjectEnsembleen
dc.subjectResamplingen
dc.subjectBig Dataen
dc.subjectSparken
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleExperimental evaluation of ensemble classifiers for imbalance in Big Dataen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.asoc.2021.107447es
dc.identifier.doi10.1016/j.asoc.2021.107447
dc.relation.projectIDinfo:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FPR18%2F51130007es
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisadoes
dc.journal.titleApplied Soft Computinges
dc.volume.number108es
dc.page.initial107447es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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