Optimizing feature selection through binary charged system search
Autor(a) principal: | |
---|---|
Data de Publicação: | 2013 |
Outros Autores: | , , , , |
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 |