Optimizing feature selection through binary charged system search

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Douglas [UNESP]
Data de Publicação: 2013
Outros Autores: Pereira, Luis A. M. [UNESP], Papa, João Paulo [UNESP], Ramos, Caio C. O. [UNESP], Souza, Andre N., Papa, Luciene P.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-642-40261-6_45
http://hdl.handle.net/11449/76647
Resumo: Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.
id UNSP_ecd8d56d1532d56423a623821956db8e
oai_identifier_str oai:repositorio.unesp.br:11449/76647
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Optimizing feature selection through binary charged system searchCharged System SearchEvolutionary OptimizationFeature FelectionCharged system searchesEvolutionary optimizationsOptimization problemsOptimum-path forestsSelection techniquesWrapper approachImage analysisOptimizationFeature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.UNESP - Univ. Estadual Paulista Department of Computing, BauruUNESP - Univ. Estadual Paulista Depart. of Electrical Engineering, BauruUniversity of São Paulo Polytechnic School, São PauloFaculdade Sudoeste Paulista Department of Health, AvaréUNESP - Univ. Estadual Paulista Department of Computing, BauruUNESP - Univ. Estadual Paulista Depart. of Electrical Engineering, BauruUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Faculdade Sudoeste PaulistaRodrigues, Douglas [UNESP]Pereira, Luis A. M. [UNESP]Papa, João Paulo [UNESP]Ramos, Caio C. O. [UNESP]Souza, Andre N.Papa, Luciene P.2014-05-27T11:30:45Z2014-05-27T11:30:45Z2013-09-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject377-384http://dx.doi.org/10.1007/978-3-642-40261-6_45Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8047 LNCS, n. PART 1, p. 377-384, 2013.0302-97431611-3349http://hdl.handle.net/11449/7664710.1007/978-3-642-40261-6_452-s2.0-848844915058212775960494686Scopusreponame: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/openAccess2024-06-28T13:34:35Zoai:repositorio.unesp.br:11449/76647Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:44:27.363393Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Optimizing feature selection through binary charged system search
title Optimizing feature selection through binary charged system search
spellingShingle Optimizing feature selection through binary charged system search
Rodrigues, Douglas [UNESP]
Charged System Search
Evolutionary Optimization
Feature Felection
Charged system searches
Evolutionary optimizations
Optimization problems
Optimum-path forests
Selection techniques
Wrapper approach
Image analysis
Optimization
title_short Optimizing feature selection through binary charged system search
title_full Optimizing feature selection through binary charged system search
title_fullStr Optimizing feature selection through binary charged system search
title_full_unstemmed Optimizing feature selection through binary charged system search
title_sort Optimizing feature selection through binary charged system search
author Rodrigues, Douglas [UNESP]
author_facet Rodrigues, Douglas [UNESP]
Pereira, Luis A. M. [UNESP]
Papa, João Paulo [UNESP]
Ramos, Caio C. O. [UNESP]
Souza, Andre N.
Papa, Luciene P.
author_role author
author2 Pereira, Luis A. M. [UNESP]
Papa, João Paulo [UNESP]
Ramos, Caio C. O. [UNESP]
Souza, Andre N.
Papa, Luciene P.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
Faculdade Sudoeste Paulista
dc.contributor.author.fl_str_mv Rodrigues, Douglas [UNESP]
Pereira, Luis A. M. [UNESP]
Papa, João Paulo [UNESP]
Ramos, Caio C. O. [UNESP]
Souza, Andre N.
Papa, Luciene P.
dc.subject.por.fl_str_mv Charged System Search
Evolutionary Optimization
Feature Felection
Charged system searches
Evolutionary optimizations
Optimization problems
Optimum-path forests
Selection techniques
Wrapper approach
Image analysis
Optimization
topic Charged System Search
Evolutionary Optimization
Feature Felection
Charged system searches
Evolutionary optimizations
Optimization problems
Optimum-path forests
Selection techniques
Wrapper approach
Image analysis
Optimization
description Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.
publishDate 2013
dc.date.none.fl_str_mv 2013-09-26
2014-05-27T11:30:45Z
2014-05-27T11:30:45Z
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/10.1007/978-3-642-40261-6_45
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8047 LNCS, n. PART 1, p. 377-384, 2013.
0302-9743
1611-3349
http://hdl.handle.net/11449/76647
10.1007/978-3-642-40261-6_45
2-s2.0-84884491505
8212775960494686
url http://dx.doi.org/10.1007/978-3-642-40261-6_45
http://hdl.handle.net/11449/76647
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8047 LNCS, n. PART 1, p. 377-384, 2013.
0302-9743
1611-3349
10.1007/978-3-642-40261-6_45
2-s2.0-84884491505
8212775960494686
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 377-384
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_ 1808128851763527680