Efficient supervised optimum-path forest classification for large datasets

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dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorFalcao, Alexandre X.
dc.contributor.authorde Albuquerque, Victor Hugo C.
dc.contributor.authorTavares, Joao Manuel R. S.
dc.contributor.institutionUniv Porto
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniv Fortaleza
dc.date.accessioned2014-05-20T13:25:57Z
dc.date.available2014-05-20T13:25:57Z
dc.date.issued2012-01-01
dc.description.abstractToday data acquisition technologies come up with large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient without losing their effectiveness. We have tried to circumvent the problem by reducing it into the fast computation of an optimum-path forest (OPF) in a graph derived from the training samples. In this forest, each class may be represented by multiple trees rooted at some representative samples. The forest is a classifier that assigns to a new sample the label of its most strongly connected root. The methodology has been successfully used with different graph topologies and learning techniques. In this work, we have focused on one of the supervised approaches, which has offered considerable advantages over Support Vector Machines and Artificial Neural Networks to handle large datasets. We propose (i) a new algorithm that speeds up classification and (ii) a solution to reduce the training set size with negligible effects on the accuracy of classification, therefore further increasing its efficiency. Experimental results show the improvements with respect to our previous approach and advantages over other existing methods, which make the new method a valuable contribution for large dataset analysis. (C) 2011 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv Porto, Fac Egenharia, P-4100 Oporto, Portugal
dc.description.affiliationUNESP Univ Estadual Paulista, Dept Comp, Bauru, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Comp, Campinas, Brazil
dc.description.affiliationUniv Fortaleza, Ctr Ciencias Tecnol, Fortaleza, Ceara, Brazil
dc.description.affiliationUnespUNESP Univ Estadual Paulista, Dept Comp, Bauru, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP)
dc.description.sponsorshipIdFAPESP: 09/16206-1
dc.description.sponsorshipIdFAPESP: 07/52015-0
dc.description.sponsorshipIdCNPq: 481556/2009-5
dc.description.sponsorshipIdCNPq: 303673/2010-91
dc.description.sponsorshipIdFUNCAP: 35.0053/2011.1
dc.format.extent512-520
dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2011.07.013
dc.identifier.citationPattern Recognition. Oxford: Elsevier B.V., v. 45, n. 1, p. 512-520, 2012.
dc.identifier.doi10.1016/j.patcog.2011.07.013
dc.identifier.issn0031-3203
dc.identifier.lattes9039182932747194
dc.identifier.urihttp://hdl.handle.net/11449/8284
dc.identifier.wosWOS:000295760700042
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofPattern Recognition
dc.relation.ispartofjcr3.962
dc.relation.ispartofsjr1,065
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectOptimum-path forest classifiersen
dc.subjectSupport vector machinesen
dc.subjectArtificial neural networksen
dc.subjectPattern recognitionen
dc.subjectMachine learningen
dc.titleEfficient supervised optimum-path forest classification for large datasetsen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes9039182932747194
unesp.author.orcid0000-0002-6494-7514[1]
unesp.author.orcid0000-0001-7603-6526[4]
unesp.author.orcid0000-0003-3886-4309[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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