A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/196009 |
Resumo: | The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of Off ensembles and the framework to design multiple classifier systems. |
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Repositório Institucional da UNESP |
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A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy ApproachThe research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of Off ensembles and the framework to design multiple classifier systems.Univ Sao Paulo ICMC USP, Inst Math & Comp Sci, Sao Carlos, SP, BrazilUNESP, Dept Comp, Bauru, SP, BrazilUniv Sao Carlos DC UFSCar, Dept Comp, Sao Carlos, SP, BrazilUNESP, Dept Comp, Bauru, SP, BrazilSpringerUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Ponti-, Moacir P.Papa, Joao Paulo [UNESP]Levada, Alexandre L. M.Martin, C. S.Kim, S. W.2020-12-10T19:30:23Z2020-12-10T19:30:23Z2011-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject581-+Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 581-+, 2011.0302-9743http://hdl.handle.net/11449/196009WOS:000307257600069Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProgress In Pattern Recognition, Image Analysis, Computer Vision, And Applicationsinfo:eu-repo/semantics/openAccess2024-04-23T16:11:12Zoai:repositorio.unesp.br:11449/196009Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
title |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
spellingShingle |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach Ponti-, Moacir P. |
title_short |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
title_full |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
title_fullStr |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
title_full_unstemmed |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
title_sort |
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach |
author |
Ponti-, Moacir P. |
author_facet |
Ponti-, Moacir P. Papa, Joao Paulo [UNESP] Levada, Alexandre L. M. Martin, C. S. Kim, S. W. |
author_role |
author |
author2 |
Papa, Joao Paulo [UNESP] Levada, Alexandre L. M. Martin, C. S. Kim, S. W. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) Universidade Federal de São Carlos (UFSCar) |
dc.contributor.author.fl_str_mv |
Ponti-, Moacir P. Papa, Joao Paulo [UNESP] Levada, Alexandre L. M. Martin, C. S. Kim, S. W. |
description |
The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of Off ensembles and the framework to design multiple classifier systems. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-01 2020-12-10T19:30:23Z 2020-12-10T19:30:23Z |
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 |
Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 581-+, 2011. 0302-9743 http://hdl.handle.net/11449/196009 WOS:000307257600069 |
identifier_str_mv |
Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 581-+, 2011. 0302-9743 WOS:000307257600069 |
url |
http://hdl.handle.net/11449/196009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
581-+ |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
Web of Science 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 |
|
_version_ |
1799964513123958784 |