Using boosting to prune bagging ensembles

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Change...

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Detalles Bibliográficos
Autores: Martínez Muñoz, Gonzalo, Suárez González, Alberto
Tipo de recurso: artículo
Fecha de publicación:2007
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/664134
Acceso en línea:http://hdl.handle.net/10486/664134
https://dx.doi.org/10.1016/j.patrec.2006.06.018
Access Level:acceso abierto
Palabra clave:Bagging
Boosting
Decision trees
Ensemble pruning
Ensembles
Machine learning
Informática
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spelling Using boosting to prune bagging ensemblesMartínez Muñoz, GonzaloSuárez González, AlbertoBaggingBoostingDecision treesEnsemble pruningEnsemblesMachine learningInformáticaThis is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.1 (2007): 156 – 165, DOI: 10.1016/j.patrec.2006.06.018Boosting is used to determine the order in which classifiers are aggregated in a bagging ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging ensemble allows the identification of subensembles that require less memory for storage, classify faster and can improve the generalization accuracy of the original bagging ensemble. In all the classification problems investigated pruned ensembles with 20 % of the original classifiers show statistically significant improvements over bagging. In problems where boosting is superior to bagging, these improvements are not sufficient to reach the accuracy of the corresponding boosting ensembles. However, ensemble pruning preserves the performance of bagging in noisy classification tasks, where boosting often has larger generalization errors. Therefore, pruned bagging should generally be preferred to complete bagging and, if no information about the level of noise is available, it is a robust alternative to AdaBoost.The authors acknowledge financial support from the Spanish Dirección General de Investigación, project TIN2004-07676-C02-02.Elsevier BVDepartamento de Ingeniería InformáticaEscuela Politécnica SuperiorAprendizaje Automático (ING EPS-001)20072007-01-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/664134https://dx.doi.org/10.1016/j.patrec.2006.06.018reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6641342026-06-23T12:46:27Z
dc.title.none.fl_str_mv Using boosting to prune bagging ensembles
title Using boosting to prune bagging ensembles
spellingShingle Using boosting to prune bagging ensembles
Martínez Muñoz, Gonzalo
Bagging
Boosting
Decision trees
Ensemble pruning
Ensembles
Machine learning
Informática
title_short Using boosting to prune bagging ensembles
title_full Using boosting to prune bagging ensembles
title_fullStr Using boosting to prune bagging ensembles
title_full_unstemmed Using boosting to prune bagging ensembles
title_sort Using boosting to prune bagging ensembles
dc.creator.none.fl_str_mv Martínez Muñoz, Gonzalo
Suárez González, Alberto
author Martínez Muñoz, Gonzalo
author_facet Martínez Muñoz, Gonzalo
Suárez González, Alberto
author_role author
author2 Suárez González, Alberto
author2_role author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
Aprendizaje Automático (ING EPS-001)
dc.subject.none.fl_str_mv Bagging
Boosting
Decision trees
Ensemble pruning
Ensembles
Machine learning
Informática
topic Bagging
Boosting
Decision trees
Ensemble pruning
Ensembles
Machine learning
Informática
description This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.1 (2007): 156 – 165, DOI: 10.1016/j.patrec.2006.06.018
publishDate 2007
dc.date.none.fl_str_mv 2007
2007-01-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/664134
https://dx.doi.org/10.1016/j.patrec.2006.06.018
url http://hdl.handle.net/10486/664134
https://dx.doi.org/10.1016/j.patrec.2006.06.018
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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