Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms

This paper proposes a novel approach to select the individual classifiers to take part in a Multiple-Classifier System. Individual classifier selection is a key step in the development of multi-classifiers. Several works have shown the benefits of fusing complementary classifiers. Nevertheless, the...

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Detalles Bibliográficos
Autores: Mendialdua Beitia, Iñigo, Arruti Illarramendi, Andoni, Jauregi Iztueta, Ekaitz, Lazkano Ortega, Elena, Sierra Araujo, Basilio
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/63985
Acceso en línea:http://hdl.handle.net/10810/63985
Access Level:acceso abierto
Palabra clave:machine learning
multiple-classifier systems
evolutionary computation
classifier subset selection
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spelling Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithmsMendialdua Beitia, IñigoArruti Illarramendi, AndoniJauregi Iztueta, EkaitzLazkano Ortega, ElenaSierra Araujo, Basiliomachine learningmultiple-classifier systemsevolutionary computationclassifier subset selectionThis paper proposes a novel approach to select the individual classifiers to take part in a Multiple-Classifier System. Individual classifier selection is a key step in the development of multi-classifiers. Several works have shown the benefits of fusing complementary classifiers. Nevertheless, the selection of the base classifiers to be used is still an open question, and different approaches have been proposed in the literature. This work is based on the selection of the appropriate single classifiers by means of an evolutionary algorithm. Different base classifiers, which have been chosen from different classifier families, are used as candidates in order to obtain variability in the classifications given. Experimental results carried out with 20 databases from the UCI Repository show how adequate the proposed approach is; Stacked Generalization multi-classifier has been selected to perform the experimental comparisons.The work described in this paper was partially conducted within the Basque Government Research Team grant and the University of the Basque Country UPV/EHU and under grant UFI11/45 (BAILab).Elsevier202420242015info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/63985reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.sciencedirect.com/science/article/abs/pii/S0925231215000570info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2015 Elsevierunder CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).oai:addi.ehu.eus:10810/639852026-06-18T09:23:17Z
dc.title.none.fl_str_mv Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
title Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
spellingShingle Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
Mendialdua Beitia, Iñigo
machine learning
multiple-classifier systems
evolutionary computation
classifier subset selection
title_short Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
title_full Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
title_fullStr Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
title_full_unstemmed Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
title_sort Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
dc.creator.none.fl_str_mv Mendialdua Beitia, Iñigo
Arruti Illarramendi, Andoni
Jauregi Iztueta, Ekaitz
Lazkano Ortega, Elena
Sierra Araujo, Basilio
author Mendialdua Beitia, Iñigo
author_facet Mendialdua Beitia, Iñigo
Arruti Illarramendi, Andoni
Jauregi Iztueta, Ekaitz
Lazkano Ortega, Elena
Sierra Araujo, Basilio
author_role author
author2 Arruti Illarramendi, Andoni
Jauregi Iztueta, Ekaitz
Lazkano Ortega, Elena
Sierra Araujo, Basilio
author2_role author
author
author
author
dc.subject.none.fl_str_mv machine learning
multiple-classifier systems
evolutionary computation
classifier subset selection
topic machine learning
multiple-classifier systems
evolutionary computation
classifier subset selection
description This paper proposes a novel approach to select the individual classifiers to take part in a Multiple-Classifier System. Individual classifier selection is a key step in the development of multi-classifiers. Several works have shown the benefits of fusing complementary classifiers. Nevertheless, the selection of the base classifiers to be used is still an open question, and different approaches have been proposed in the literature. This work is based on the selection of the appropriate single classifiers by means of an evolutionary algorithm. Different base classifiers, which have been chosen from different classifier families, are used as candidates in order to obtain variability in the classifications given. Experimental results carried out with 20 databases from the UCI Repository show how adequate the proposed approach is; Stacked Generalization multi-classifier has been selected to perform the experimental comparisons.
publishDate 2015
dc.date.none.fl_str_mv 2015
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/63985
url http://hdl.handle.net/10810/63985
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S0925231215000570
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
© 2015 Elsevierunder CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
© 2015 Elsevierunder CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403686492438528
score 15,301603

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