Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics

Simple item page
dc.contributor.authorLopez-Garcia, Pedro
dc.contributor.authorMasegosa, Antonio D.
dc.contributor.authorOsaba, Eneko
dc.contributor.authorOnieva, Enrique
dc.contributor.authorPerallos, Asier
dc.contributor.institutionQuantum
dc.date.issued2019-08-15
dc.descriptionPublisher Copyright: © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
dc.description.abstractOne of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature.en
dc.description.sponsorshipThis work has been supported by the research projects TEC2013-45585-C2-2-R and TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness, the TIMON project, which received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 636220, and the LOGISTAR project, which received funding from European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769142. This work has been supported by the research projects TEC2013-45585-C2-2-R and TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness, the TIMON project, which received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 636220, and the LOGISTAR project, which received funding from European Union?s Horizon 2020 research and innovation programme under grant agreement No. 769142.
dc.description.statusPeer reviewed
dc.format.extent16
dc.identifier.citationLopez-Garcia , P , Masegosa , A D , Osaba , E , Onieva , E & Perallos , A 2019 , ' Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics ' , Applied Intelligence , vol. 49 , no. 8 , pp. 2807-2822 . https://doi.org/10.1007/s10489-019-01423-6
dc.identifier.doi10.1007/s10489-019-01423-6
dc.identifier.issn0924-669X
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85061199908&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Intelligence
dc.relation.projectIDEuropean Union Horizon 2020
dc.relation.projectIDEuropean Union?s Horizon 2020
dc.relation.projectIDSpanish Ministry of Economy and Competitiveness
dc.relation.projectIDHorizon 2020 Framework Programme, H2020, 636220-769142
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsEnsemble classification
dc.subject.keywordsFeature space partitioning
dc.subject.keywordsHybrid metaheuristics
dc.subject.keywordsImbalanced classification
dc.subject.keywordsArtificial Intelligence
dc.titleEnsemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristicsen
dc.typejournal article
Files