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dc.creatorNúñez Castro, Haydemares
dc.creatorGonzález Abril, Luises
dc.creatorAngulo Bahón, Cecilioes
dc.date.accessioned2018-11-05T11:58:10Z
dc.date.available2018-11-05T11:58:10Z
dc.date.issued2011
dc.identifier.citationNúñez Castro, H., González Abril, L. y Angulo Bahón, C. (2011). A post-processing strategy for SVM learning from unbalanced data. En 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (195-200), Bruges: Ciaco.
dc.identifier.isbn978-2-87419-044-5es
dc.identifier.urihttps://hdl.handle.net/11441/79797
dc.descriptionEstá en: https://upcommons.upc.edu/handle/2117/12531
dc.description.abstractStandard learning algorithms may perform poorly when learning from unbalanced datasets. Based on the Fisher’s discriminant analysis, a post-processing strategy is introduced to deal datasets with significant imbalance in the data distribution. A new bias is defined, which reduces skew towards the minority class. Empirical results from experiments for a learned SVM model on twelve UCI datasets indicates that the proposed solution improves the original SVM, and they also improve those reported when using a z-SVM, in terms of g-mean and sensitivity.es
dc.description.sponsorshipSpanish Ministry of Science and Technology TIN2009-14378-C02-01es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherCiacoes
dc.relation.ispartof19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2011), p 195-200
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA post-processing strategy for SVM learning from unbalanced dataes
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Economía Aplicada Ies
dc.relation.projectIDTIN2009-14378-C02-01es
dc.contributor.groupUniversidad de Sevilla. SEJ442: Métodos Cualitativos y Optimización en Sistemas Dinámicos Económicoses
idus.format.extent6 p.es
dc.publication.initialPage195es
dc.publication.endPage200es
dc.eventtitle19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learninges
dc.eventinstitutionBrugeses
dc.relation.publicationplaceLouvain-la-Neuvees
dc.contributor.funderMinisterio de Ciencia y Tecnología (MCYT). España

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional