Particle Swarm based Data Mining Algorithms for classification tasks
Autor(a) principal: | |
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Data de Publicação: | 2004 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/4105 https://doi.org/10.1016/j.parco.2003.12.015 |
Resumo: | Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains. |
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Particle Swarm based Data Mining Algorithms for classification tasksData MiningParticle Swarm OptimisationSwarm intelligenceParticle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.http://www.sciencedirect.com/science/article/B6V12-4CDJKX0-1/1/4d6f3996ada8de80b22275b081f214632004info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleaplication/PDFhttp://hdl.handle.net/10316/4105http://hdl.handle.net/10316/4105https://doi.org/10.1016/j.parco.2003.12.015engParallel Computing. 30:5-6 (2004) 767-783Sousa, TiagoSilva, ArlindoNeves, Anainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-11-06T16:59:13Zoai:estudogeral.uc.pt:10316/4105Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:58:17.566987Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Particle Swarm based Data Mining Algorithms for classification tasks |
title |
Particle Swarm based Data Mining Algorithms for classification tasks |
spellingShingle |
Particle Swarm based Data Mining Algorithms for classification tasks Sousa, Tiago Data Mining Particle Swarm Optimisation Swarm intelligence |
title_short |
Particle Swarm based Data Mining Algorithms for classification tasks |
title_full |
Particle Swarm based Data Mining Algorithms for classification tasks |
title_fullStr |
Particle Swarm based Data Mining Algorithms for classification tasks |
title_full_unstemmed |
Particle Swarm based Data Mining Algorithms for classification tasks |
title_sort |
Particle Swarm based Data Mining Algorithms for classification tasks |
author |
Sousa, Tiago |
author_facet |
Sousa, Tiago Silva, Arlindo Neves, Ana |
author_role |
author |
author2 |
Silva, Arlindo Neves, Ana |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Sousa, Tiago Silva, Arlindo Neves, Ana |
dc.subject.por.fl_str_mv |
Data Mining Particle Swarm Optimisation Swarm intelligence |
topic |
Data Mining Particle Swarm Optimisation Swarm intelligence |
description |
Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/4105 http://hdl.handle.net/10316/4105 https://doi.org/10.1016/j.parco.2003.12.015 |
url |
http://hdl.handle.net/10316/4105 https://doi.org/10.1016/j.parco.2003.12.015 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Parallel Computing. 30:5-6 (2004) 767-783 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
aplication/PDF |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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