Fast Feature Ranking Algorithm

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm has some interesting characteristics: lower computational cost (O(m n log n) m at...

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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:2003
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/39228
Acceso en línea:http://hdl.handle.net/11441/39228
https://doi.org/10.1007/978-3-540-45224-9_46
Access Level:acceso abierto
Palabra clave:Artificial Intelligence (incl. Robotics)
Computer Communication Networks
Information Storage and Retrieval
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
IT in Business
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spelling Fast Feature Ranking AlgorithmRuiz Sánchez, RobertoRiquelme Santos, José CristóbalAguilar Ruiz, Jesús SalvadorArtificial Intelligence (incl. Robotics)Computer Communication NetworksInformation Storage and RetrievalInformation Systems Applications (incl. Internet)User Interfaces and Human Computer InteractionIT in BusinessThe attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. In order to test the relevance of the new feature selection algorithm, we compare the results induced by several classifiers before and after applying the feature selection algorithms.Lenguajes y Sistemas Informáticos2003info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/39228https://doi.org/10.1007/978-3-540-45224-9_46reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésKnowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Volume 2773, pp 325-331 (2003)info:eu-repo/semantics/openAccessoai:idus.us.es:11441/392282026-06-17T12:51:07Z
dc.title.none.fl_str_mv Fast Feature Ranking Algorithm
title Fast Feature Ranking Algorithm
spellingShingle Fast Feature Ranking Algorithm
Ruiz Sánchez, Roberto
Artificial Intelligence (incl. Robotics)
Computer Communication Networks
Information Storage and Retrieval
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
IT in Business
title_short Fast Feature Ranking Algorithm
title_full Fast Feature Ranking Algorithm
title_fullStr Fast Feature Ranking Algorithm
title_full_unstemmed Fast Feature Ranking Algorithm
title_sort Fast Feature Ranking Algorithm
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 (incl. Robotics)
Computer Communication Networks
Information Storage and Retrieval
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
IT in Business
topic Artificial Intelligence (incl. Robotics)
Computer Communication Networks
Information Storage and Retrieval
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
IT in Business
description The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. In order to test the relevance of the new feature selection algorithm, we compare the results induced by several classifiers before and after applying the feature selection algorithms.
publishDate 2003
dc.date.none.fl_str_mv 2003
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/11441/39228
https://doi.org/10.1007/978-3-540-45224-9_46
url http://hdl.handle.net/11441/39228
https://doi.org/10.1007/978-3-540-45224-9_46
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Volume 2773, pp 325-331 (2003)
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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