Feature selection through gravitational search algorithm
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://dx.doi.org/10.1109/ICASSP.2011.5946916 http://hdl.handle.net/11449/12545 |
Resumo: | In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection. |
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Repositório Institucional da UNESP |
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Feature selection through gravitational search algorithmFeature selectionPattern classificationOptimum-Path ForestGravitational Search AlgorithmIn this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.UNESP Univ Estadual Paulista, Dept Comp, São Paulo, BrazilUNESP Univ Estadual Paulista, Dept Comp, São Paulo, BrazilIEEEUniversidade Estadual Paulista (Unesp)Papa, J. P. [UNESP]Pagnin, A. [UNESP]Schellini, Silvana Artioli [UNESP]Spadotto, A.Guido, R. C.Ponti, M.Chiachia, G.Falcao, A. X.2014-05-20T13:36:26Z2014-05-20T13:36:26Z2011-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2052-2055http://dx.doi.org/10.1109/ICASSP.2011.59469162011 IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, p. 2052-2055, 2011.1520-6149http://hdl.handle.net/11449/1254510.1109/ICASSP.2011.5946916WOS:000296062402094942024910083549265420862268080670000-0002-0924-8024Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2011 IEEE International Conference on Acoustics, Speech, and Signal Processinginfo:eu-repo/semantics/openAccess2024-08-16T18:44:54Zoai:repositorio.unesp.br:11449/12545Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-16T18:44:54Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Feature selection through gravitational search algorithm |
title |
Feature selection through gravitational search algorithm |
spellingShingle |
Feature selection through gravitational search algorithm Papa, J. P. [UNESP] Feature selection Pattern classification Optimum-Path Forest Gravitational Search Algorithm |
title_short |
Feature selection through gravitational search algorithm |
title_full |
Feature selection through gravitational search algorithm |
title_fullStr |
Feature selection through gravitational search algorithm |
title_full_unstemmed |
Feature selection through gravitational search algorithm |
title_sort |
Feature selection through gravitational search algorithm |
author |
Papa, J. P. [UNESP] |
author_facet |
Papa, J. P. [UNESP] Pagnin, A. [UNESP] Schellini, Silvana Artioli [UNESP] Spadotto, A. Guido, R. C. Ponti, M. Chiachia, G. Falcao, A. X. |
author_role |
author |
author2 |
Pagnin, A. [UNESP] Schellini, Silvana Artioli [UNESP] Spadotto, A. Guido, R. C. Ponti, M. Chiachia, G. Falcao, A. X. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Papa, J. P. [UNESP] Pagnin, A. [UNESP] Schellini, Silvana Artioli [UNESP] Spadotto, A. Guido, R. C. Ponti, M. Chiachia, G. Falcao, A. X. |
dc.subject.por.fl_str_mv |
Feature selection Pattern classification Optimum-Path Forest Gravitational Search Algorithm |
topic |
Feature selection Pattern classification Optimum-Path Forest Gravitational Search Algorithm |
description |
In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-01 2014-05-20T13:36:26Z 2014-05-20T13:36:26Z |
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/ICASSP.2011.5946916 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, p. 2052-2055, 2011. 1520-6149 http://hdl.handle.net/11449/12545 10.1109/ICASSP.2011.5946916 WOS:000296062402094 9420249100835492 6542086226808067 0000-0002-0924-8024 |
url |
http://dx.doi.org/10.1109/ICASSP.2011.5946916 http://hdl.handle.net/11449/12545 |
identifier_str_mv |
2011 IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, p. 2052-2055, 2011. 1520-6149 10.1109/ICASSP.2011.5946916 WOS:000296062402094 9420249100835492 6542086226808067 0000-0002-0924-8024 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2011 IEEE International Conference on Acoustics, Speech, and Signal Processing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2052-2055 |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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_ |
1808128140396986368 |