Improving optimum-path forest learning using bag-of-classifiers and confidence measures

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dc.contributor.authorFernandes, Silas Evandro Nachif
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:35:13Z
dc.date.available2018-12-11T17:35:13Z
dc.date.issued2017-12-18
dc.description.abstractMachine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this work, we presented an ensemble of optimum-path forest (OPF) classifiers, which consists into combining different instances that compute a score-based confidence level for each training sample in order to turn the classification process “smarter”, i.e., more reliable. Such confidence level encodes the level of effectiveness of each training sample, and it can be used to avoid ties during the OPF competition process. Experimental results over fifteen benchmarking datasets have shown the effectiveness and efficiency of the proposed approach for classification problems, with more accurate results in more than 67% of the datasets considered in this work. Additionally, we also considered a bagging strategy for comparison purposes, and we showed the proposed approach can lead to considerably better results.en
dc.description.affiliationDepartment of Computing Federal University of São Carlos - UFSCar, Rodovia Washington Luís, Km 235 - SP 310
dc.description.affiliationDepartment of Computing São Paulo State University - UNESP, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01
dc.description.affiliationUnespDepartment of Computing São Paulo State University - UNESP, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01
dc.format.extent1-14
dc.identifierhttp://dx.doi.org/10.1007/s10044-017-0677-9
dc.identifier.citationPattern Analysis and Applications, p. 1-14.
dc.identifier.doi10.1007/s10044-017-0677-9
dc.identifier.file2-s2.0-85038372005.pdf
dc.identifier.issn1433-7541
dc.identifier.scopus2-s2.0-85038372005
dc.identifier.urihttp://hdl.handle.net/11449/179444
dc.language.isoeng
dc.relation.ispartofPattern Analysis and Applications
dc.relation.ispartofsjr0,378
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectClassifier ensemble
dc.subjectOptimum-path forest
dc.subjectSupervised learning
dc.titleImproving optimum-path forest learning using bag-of-classifiers and confidence measuresen
dc.typeArtigo
unesp.author.orcid0000-0002-6494-7514[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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