Improving semi-supervised learning through optimum connectivity

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dc.contributor.authorAmorim, Willian P.
dc.contributor.authorFalcao, Alexandre X.
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.authorCarvalho, Marcelo H.
dc.contributor.institutionUniversidade Federal de Mato Grosso do Sul (UFMS)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T17:06:13Z
dc.date.available2018-11-26T17:06:13Z
dc.date.issued2016-12-01
dc.description.abstractThe annotation of large data sets by a classifier is a problem whose challenge increases as the number of labeled samples used to train the classifier reduces in comparison to the number of unlabeled samples. In this context, semi-supervised learning methods aim at discovering and labeling informative samples among the unlabeled ones, such that their addition to the correct class in the training set can improve classification performance. We present a semi-supervised learning approach that connects unlabeled and labeled samples as nodes of a minimum-spanning tree and partitions the tree into an optimum-path forest rooted at the labeled nodes. It is suitable when most samples from a same class are more closely connected through sequences of nearby samples than samples from distinct classes, which is usually the case in data sets with a reasonable relation between number of samples and feature space dimension. The proposed solution is validated by using several data sets and state-of-the-art methods as baselines. (C) 2016 Elsevier Ltd. All rights reserved.en
dc.description.affiliationFed Univ Mato Grosso UFMS, FACOM Inst Comp, Cidade Univ, BR-79070900 Campo Grande, MS, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Comp, Dept Informat Syst, Av Albert Einstein 1251, BR-13083852 Campinas, SP, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdCNPq: 303673/2010-9
dc.description.sponsorshipIdCNPq: 479070/2013-0
dc.description.sponsorshipIdCNPq: 302970/2014-2
dc.description.sponsorshipIdCNPq: 303182/2011-3
dc.description.sponsorshipIdCNPq: 470571/2013-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.description.sponsorshipIdFAPESP: 2013/20387-7
dc.description.sponsorshipIdFAPESP: 2014/16250-9
dc.format.extent72-85
dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2016.04.020
dc.identifier.citationPattern Recognition. Oxford: Elsevier Sci Ltd, v. 60, p. 72-85, 2016.
dc.identifier.doi10.1016/j.patcog.2016.04.020
dc.identifier.fileWOS000383525600008.pdf
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/11449/161931
dc.identifier.wosWOS:000383525600008
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofPattern Recognition
dc.relation.ispartofsjr1,065
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectSemi-supervised learning
dc.subjectOptimum-path forest classifiers
dc.titleImproving semi-supervised learning through optimum connectivityen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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