Analysis of Feature Rankings for Classification

Different ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several possible interpretations, depending on the given approach to each study. We begin from the premise of no existence of only one ideal subset for all cases. The purpose of these k...

Descripción completa

Detalles Bibliográficos
Autores: Ruiz Sánchez, Roberto, Aguilar Ruiz, Jesús Salvador, Riquelme Santos, José Cristóbal, Díaz Díaz, Norberto
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:11441/39727
Acceso en línea:http://hdl.handle.net/11441/39727
https://doi.org/10.1007/11552253_33
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Information storage and retrieval
Probability and statistics in Computer Science
Pattern recognition
id ES_55c18af559a3707e602942a7a1aaa11a
oai_identifier_str oai:idus.us.es:11441/39727
network_acronym_str ES
network_name_str España
repository_id_str
spelling Analysis of Feature Rankings for ClassificationRuiz Sánchez, RobertoAguilar Ruiz, Jesús SalvadorRiquelme Santos, José CristóbalDíaz Díaz, NorbertoArtificial intelligenceInformation storage and retrievalProbability and statistics in Computer SciencePattern recognitionDifferent ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several possible interpretations, depending on the given approach to each study. We begin from the premise of no existence of only one ideal subset for all cases. The purpose of these kinds of algorithms is to reduce the data set to each first attributes without losing prediction against the original data set. In this paper we propose a method, feature–ranking performance, to compare different feature–ranking methods, based on the Area Under Feature Ranking Classification Performance Curve (AURC). Conclusions and trends taken from this paper propose support for the performance of learning tasks, where some ranking algorithms studied here operate.Lenguajes y Sistemas Informáticos2005info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/39727https://doi.org/10.1007/11552253_33reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésAdvances in Intelligent Data Analysis VI, Lecture Notes in Computer Science, Volume 3646, pp 362-372 (2005)info:eu-repo/semantics/openAccessoai:idus.us.es:11441/397272026-06-17T12:51:07Z
dc.title.none.fl_str_mv Analysis of Feature Rankings for Classification
title Analysis of Feature Rankings for Classification
spellingShingle Analysis of Feature Rankings for Classification
Ruiz Sánchez, Roberto
Artificial intelligence
Information storage and retrieval
Probability and statistics in Computer Science
Pattern recognition
title_short Analysis of Feature Rankings for Classification
title_full Analysis of Feature Rankings for Classification
title_fullStr Analysis of Feature Rankings for Classification
title_full_unstemmed Analysis of Feature Rankings for Classification
title_sort Analysis of Feature Rankings for Classification
dc.creator.none.fl_str_mv Ruiz Sánchez, Roberto
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Díaz Díaz, Norberto
author Ruiz Sánchez, Roberto
author_facet Ruiz Sánchez, Roberto
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Díaz Díaz, Norberto
author_role author
author2 Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Díaz Díaz, Norberto
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Artificial intelligence
Information storage and retrieval
Probability and statistics in Computer Science
Pattern recognition
topic Artificial intelligence
Information storage and retrieval
Probability and statistics in Computer Science
Pattern recognition
description Different ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several possible interpretations, depending on the given approach to each study. We begin from the premise of no existence of only one ideal subset for all cases. The purpose of these kinds of algorithms is to reduce the data set to each first attributes without losing prediction against the original data set. In this paper we propose a method, feature–ranking performance, to compare different feature–ranking methods, based on the Area Under Feature Ranking Classification Performance Curve (AURC). Conclusions and trends taken from this paper propose support for the performance of learning tasks, where some ranking algorithms studied here operate.
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/11441/39727
https://doi.org/10.1007/11552253_33
url http://hdl.handle.net/11441/39727
https://doi.org/10.1007/11552253_33
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Advances in Intelligent Data Analysis VI, Lecture Notes in Computer Science, Volume 3646, pp 362-372 (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
_version_ 1869408329980182528
score 15,301603

Similares en LA Referencia