Pruning optimum-path forest ensembles using quaternion-based optimization
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2017.7965959 http://hdl.handle.net/11449/232660 |
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 context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together. In this paper, we present an ensemble pruning approach of Optimum-Path Forest classifiers based on metaheuristics, as well as we introduced the concept of quaternions in ensemble pruning strategies. Experimental results over synthetic and real datasets showed the effectiveness and efficiency of the proposed approach for classification problems. |
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Pruning optimum-path forest ensembles using quaternion-based optimizationMachine 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 context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together. In this paper, we present an ensemble pruning approach of Optimum-Path Forest classifiers based on metaheuristics, as well as we introduced the concept of quaternions in ensemble pruning strategies. Experimental results over synthetic and real datasets showed the effectiveness and efficiency of the proposed approach for classification problems.Department of Computing Federal University of São Carlos, Rod. Washington Luis, Km 235Department of Computing São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01Department of Computing São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (UNESP)Fernandes, Silas Evandro NachifPapa, Joao Paulo [UNESP]2022-04-30T02:38:57Z2022-04-30T02:38:57Z2017-06-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject984-991http://dx.doi.org/10.1109/IJCNN.2017.7965959Proceedings of the International Joint Conference on Neural Networks, v. 2017-May, p. 984-991.http://hdl.handle.net/11449/23266010.1109/IJCNN.2017.79659592-s2.0-85031020871Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/232660Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:47:58.425869Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Pruning optimum-path forest ensembles using quaternion-based optimization |
title |
Pruning optimum-path forest ensembles using quaternion-based optimization |
spellingShingle |
Pruning optimum-path forest ensembles using quaternion-based optimization Fernandes, Silas Evandro Nachif |
title_short |
Pruning optimum-path forest ensembles using quaternion-based optimization |
title_full |
Pruning optimum-path forest ensembles using quaternion-based optimization |
title_fullStr |
Pruning optimum-path forest ensembles using quaternion-based optimization |
title_full_unstemmed |
Pruning optimum-path forest ensembles using quaternion-based optimization |
title_sort |
Pruning optimum-path forest ensembles using quaternion-based optimization |
author |
Fernandes, Silas Evandro Nachif |
author_facet |
Fernandes, Silas Evandro Nachif Papa, Joao Paulo [UNESP] |
author_role |
author |
author2 |
Papa, Joao Paulo [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Fernandes, Silas Evandro Nachif Papa, Joao Paulo [UNESP] |
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 context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together. In this paper, we present an ensemble pruning approach of Optimum-Path Forest classifiers based on metaheuristics, as well as we introduced the concept of quaternions in ensemble pruning strategies. Experimental results over synthetic and real datasets showed the effectiveness and efficiency of the proposed approach for classification problems. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-06-30 2022-04-30T02:38:57Z 2022-04-30T02:38:57Z |
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/10.1109/IJCNN.2017.7965959 Proceedings of the International Joint Conference on Neural Networks, v. 2017-May, p. 984-991. http://hdl.handle.net/11449/232660 10.1109/IJCNN.2017.7965959 2-s2.0-85031020871 |
url |
http://dx.doi.org/10.1109/IJCNN.2017.7965959 http://hdl.handle.net/11449/232660 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 2017-May, p. 984-991. 10.1109/IJCNN.2017.7965959 2-s2.0-85031020871 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
984-991 |
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 |
|
_version_ |
1808128564243988480 |