Objective measures ensemble in associative classifiers

Detalhes bibliográficos
Autor(a) principal: Dall'Agnol, Maicon [UNESP]
Data de Publicação: 2020
Outros Autores: de Carvalho, Veronica Oliveira [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/199385
Resumo: Associative classifiers (ACs) are predictive models built based on association rules (ARs). Model construction occurs in steps, one of them aimed at sorting and pruning a set of rules. Regarding ordering, usually objective measures (OMs) are used to rank the rules. The aim of this work is exactly sorting. In the proposals found in the literature, the OMs are generally explored separately. The only work that explores the aggregation of measures in the context of ACs is (Silva and Carvalho, 2018), where multiple OMs are considered at the same time. To do so, (Silva and Carvalho, 2018) use the aggregation solution proposed by (Bouker et al., 2014). However, although there are many works in the context of ARs that investigate the aggregate use of OMs, all of them have some bias. Thus, this work aims to evaluate the aggregation of measures in the context of ACs considering another perspective, that of an ensemble of classifiers.
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spelling Objective measures ensemble in associative classifiersAssociation RulesAssociative ClassifierClassificationInterestingness MeasuresRankingAssociative classifiers (ACs) are predictive models built based on association rules (ARs). Model construction occurs in steps, one of them aimed at sorting and pruning a set of rules. Regarding ordering, usually objective measures (OMs) are used to rank the rules. The aim of this work is exactly sorting. In the proposals found in the literature, the OMs are generally explored separately. The only work that explores the aggregation of measures in the context of ACs is (Silva and Carvalho, 2018), where multiple OMs are considered at the same time. To do so, (Silva and Carvalho, 2018) use the aggregation solution proposed by (Bouker et al., 2014). However, although there are many works in the context of ARs that investigate the aggregate use of OMs, all of them have some bias. Thus, this work aims to evaluate the aggregation of measures in the context of ACs considering another perspective, that of an ensemble of classifiers.Universidade Estadual Paulista (Unesp) Instituto de Geociências e Ciências ExatasUniversidade Estadual Paulista (Unesp) Instituto de Geociências e Ciências ExatasUniversidade Estadual Paulista (Unesp)Dall'Agnol, Maicon [UNESP]de Carvalho, Veronica Oliveira [UNESP]2020-12-12T01:38:22Z2020-12-12T01:38:22Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject83-90ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems, v. 1, p. 83-90.http://hdl.handle.net/11449/1993852-s2.0-85090786983Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systemsinfo:eu-repo/semantics/openAccess2021-10-22T20:04:26Zoai:repositorio.unesp.br:11449/199385Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:21:02.600968Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Objective measures ensemble in associative classifiers
title Objective measures ensemble in associative classifiers
spellingShingle Objective measures ensemble in associative classifiers
Dall'Agnol, Maicon [UNESP]
Association Rules
Associative Classifier
Classification
Interestingness Measures
Ranking
title_short Objective measures ensemble in associative classifiers
title_full Objective measures ensemble in associative classifiers
title_fullStr Objective measures ensemble in associative classifiers
title_full_unstemmed Objective measures ensemble in associative classifiers
title_sort Objective measures ensemble in associative classifiers
author Dall'Agnol, Maicon [UNESP]
author_facet Dall'Agnol, Maicon [UNESP]
de Carvalho, Veronica Oliveira [UNESP]
author_role author
author2 de Carvalho, Veronica Oliveira [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Dall'Agnol, Maicon [UNESP]
de Carvalho, Veronica Oliveira [UNESP]
dc.subject.por.fl_str_mv Association Rules
Associative Classifier
Classification
Interestingness Measures
Ranking
topic Association Rules
Associative Classifier
Classification
Interestingness Measures
Ranking
description Associative classifiers (ACs) are predictive models built based on association rules (ARs). Model construction occurs in steps, one of them aimed at sorting and pruning a set of rules. Regarding ordering, usually objective measures (OMs) are used to rank the rules. The aim of this work is exactly sorting. In the proposals found in the literature, the OMs are generally explored separately. The only work that explores the aggregation of measures in the context of ACs is (Silva and Carvalho, 2018), where multiple OMs are considered at the same time. To do so, (Silva and Carvalho, 2018) use the aggregation solution proposed by (Bouker et al., 2014). However, although there are many works in the context of ARs that investigate the aggregate use of OMs, all of them have some bias. Thus, this work aims to evaluate the aggregation of measures in the context of ACs considering another perspective, that of an ensemble of classifiers.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T01:38:22Z
2020-12-12T01:38:22Z
2020-01-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems, v. 1, p. 83-90.
http://hdl.handle.net/11449/199385
2-s2.0-85090786983
identifier_str_mv ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems, v. 1, p. 83-90.
2-s2.0-85090786983
url http://hdl.handle.net/11449/199385
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 83-90
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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