Improving optimum-path forest classification using confidence measures

Detalhes bibliográficos
Autor(a) principal: Fernandes, Silas E. N.
Data de Publicação: 2015
Outros Autores: Scheirer, Walter, Cox, David D., Papa, Jo�o Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-319-16811-1_22
http://hdl.handle.net/11449/168923
Resumo: Machine 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 improved version of the Optimum-Path Forest classifier, which learns a score-based confidence level for each training sample in order to turn the classification process “smarter”, i.e., more reliable. Experimental results over 20 benchmarking datasets have showed the effectiveness and efficiency of the proposed approach for classification problems, which can obtain more accurate results, even on smaller training sets.
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spelling Improving optimum-path forest classification using confidence measuresConfidence measuresOptimum-path forestSupervised learningMachine 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 improved version of the Optimum-Path Forest classifier, which learns a score-based confidence level for each training sample in order to turn the classification process “smarter”, i.e., more reliable. Experimental results over 20 benchmarking datasets have showed the effectiveness and efficiency of the proposed approach for classification problems, which can obtain more accurate results, even on smaller training sets.Department of Computing Federal University of S�o Carlos UFSCar Rodovia Washington Lu�s, Km 235-SP 310Department of Molecular and Cellular Biology Harvard University, 52 Oxford StDepartment of Computing Univ Estadual Paulista-UNESP, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01Department of Computing Univ Estadual Paulista-UNESP, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01Universidade Federal de São Carlos (UFSCar)Harvard UniversityUniversidade Estadual Paulista (Unesp)Fernandes, Silas E. N.Scheirer, WalterCox, David D.Papa, Jo�o Paulo [UNESP]2018-12-11T16:43:38Z2018-12-11T16:43:38Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject619-625http://dx.doi.org/10.1007/978-3-319-16811-1_22Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 619-625.1611-33490302-9743http://hdl.handle.net/11449/16892310.1007/978-3-319-16811-1_222-s2.0-84983604654Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:21Zoai:repositorio.unesp.br:11449/168923Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462021-10-23T21:44:21Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Improving optimum-path forest classification using confidence measures
title Improving optimum-path forest classification using confidence measures
spellingShingle Improving optimum-path forest classification using confidence measures
Fernandes, Silas E. N.
Confidence measures
Optimum-path forest
Supervised learning
title_short Improving optimum-path forest classification using confidence measures
title_full Improving optimum-path forest classification using confidence measures
title_fullStr Improving optimum-path forest classification using confidence measures
title_full_unstemmed Improving optimum-path forest classification using confidence measures
title_sort Improving optimum-path forest classification using confidence measures
author Fernandes, Silas E. N.
author_facet Fernandes, Silas E. N.
Scheirer, Walter
Cox, David D.
Papa, Jo�o Paulo [UNESP]
author_role author
author2 Scheirer, Walter
Cox, David D.
Papa, Jo�o Paulo [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Carlos (UFSCar)
Harvard University
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Fernandes, Silas E. N.
Scheirer, Walter
Cox, David D.
Papa, Jo�o Paulo [UNESP]
dc.subject.por.fl_str_mv Confidence measures
Optimum-path forest
Supervised learning
topic Confidence measures
Optimum-path forest
Supervised learning
description Machine 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 improved version of the Optimum-Path Forest classifier, which learns a score-based confidence level for each training sample in order to turn the classification process “smarter”, i.e., more reliable. Experimental results over 20 benchmarking datasets have showed the effectiveness and efficiency of the proposed approach for classification problems, which can obtain more accurate results, even on smaller training sets.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2018-12-11T16:43:38Z
2018-12-11T16:43:38Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-319-16811-1_22
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 619-625.
1611-3349
0302-9743
http://hdl.handle.net/11449/168923
10.1007/978-3-319-16811-1_22
2-s2.0-84983604654
url http://dx.doi.org/10.1007/978-3-319-16811-1_22
http://hdl.handle.net/11449/168923
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 619-625.
1611-3349
0302-9743
10.1007/978-3-319-16811-1_22
2-s2.0-84983604654
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 619-625
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
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