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dc.contributor.authorNúñez Castro, Haydemar
dc.contributor.authorGonzález Abril, Luis
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2011-05-10T13:29:15Z
dc.date.available2011-05-10T13:29:15Z
dc.date.created2011
dc.date.issued2011
dc.identifier.citationNúñez, H.; González, L.; Angulo, C. A post-processing strategy for SVM learning from unbalanced data. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning". Bruges: 2011, p. 195-200.
dc.identifier.isbn978-2-87419-044-5
dc.identifier.urihttp://hdl.handle.net/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.
dc.format.extent6 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Microelectrònica::Sistemes digitals programables
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshSupport vector machines
dc.subject.lcshMachine learning--Mathematical models
dc.titleA post-processing strategy for SVM learning from unbalanced data
dc.typeConference lecture
dc.subject.lemacAprenentatge automàtic -- Algorismes
dc.subject.lemacSistemes experts (Informàtica) -- Autoaprenentatge
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.identifier.dlD2011/9262/2
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
drac.iddocument5731254
dc.description.versionPostprint (author’s final draft)
upcommons.citation.authorNúñez, H.; González, L.; Angulo, C.
upcommons.citation.contributorEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
upcommons.citation.pubplaceBruges
upcommons.citation.publishedtrue
upcommons.citation.publicationName19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
upcommons.citation.startingPage195
upcommons.citation.endingPage200


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