Heuristic Search over a Ranking for Feature Selection

In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is...

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Detalles Bibliográficos
Autores: Ruiz Sánchez, Roberto, Riquelme Santos, José Cristóbal, Aguilar Ruiz, Jesús Salvador
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2005
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/39715
Acceso en línea:http://hdl.handle.net/11441/39715
https://doi.org/10.1007/11494669_91
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Pattern recognition
Algorithm analysis
Image processing
Computer vision
Evolutionary biology
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spelling Heuristic Search over a Ranking for Feature SelectionRuiz Sánchez, RobertoRiquelme Santos, José CristóbalAguilar Ruiz, Jesús SalvadorArtificial intelligencePattern recognitionAlgorithm analysisImage processingComputer visionEvolutionary biologyIn this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is chosen if additional information is gained by adding it. This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection algorithms in twelve well–known data sets, coupled with notable dimensionality reduction.Lenguajes y Sistemas Informáticos2005info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/39715https://doi.org/10.1007/11494669_91reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputational Intelligence and Bioinspired Systems, Lecture Notes in Computer Science, Volume 3512, pp 742-749 (2005)info:eu-repo/semantics/openAccessoai:idus.us.es:11441/397152026-06-17T12:51:07Z
dc.title.none.fl_str_mv Heuristic Search over a Ranking for Feature Selection
title Heuristic Search over a Ranking for Feature Selection
spellingShingle Heuristic Search over a Ranking for Feature Selection
Ruiz Sánchez, Roberto
Artificial intelligence
Pattern recognition
Algorithm analysis
Image processing
Computer vision
Evolutionary biology
title_short Heuristic Search over a Ranking for Feature Selection
title_full Heuristic Search over a Ranking for Feature Selection
title_fullStr Heuristic Search over a Ranking for Feature Selection
title_full_unstemmed Heuristic Search over a Ranking for Feature Selection
title_sort Heuristic Search over a Ranking for Feature Selection
dc.creator.none.fl_str_mv Ruiz Sánchez, Roberto
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
author Ruiz Sánchez, Roberto
author_facet Ruiz Sánchez, Roberto
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
author_role author
author2 Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
author2_role author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Artificial intelligence
Pattern recognition
Algorithm analysis
Image processing
Computer vision
Evolutionary biology
topic Artificial intelligence
Pattern recognition
Algorithm analysis
Image processing
Computer vision
Evolutionary biology
description In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is chosen if additional information is gained by adding it. This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection algorithms in twelve well–known data sets, coupled with notable dimensionality reduction.
publishDate 2005
dc.date.none.fl_str_mv 2005
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/39715
https://doi.org/10.1007/11494669_91
url http://hdl.handle.net/11441/39715
https://doi.org/10.1007/11494669_91
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computational Intelligence and Bioinspired Systems, Lecture Notes in Computer Science, Volume 3512, pp 742-749 (2005)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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