dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-12T01:38:22Z
dc.date.accessioned2022-12-19T20:52:06Z
dc.date.available2020-12-12T01:38:22Z
dc.date.available2022-12-19T20:52:06Z
dc.date.created2020-12-12T01:38:22Z
dc.date.issued2020-01-01
dc.identifierICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems, v. 1, p. 83-90.
dc.identifierhttp://hdl.handle.net/11449/199385
dc.identifier2-s2.0-85090786983
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5380019
dc.description.abstractAssociative 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.
dc.languageeng
dc.relationICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems
dc.sourceScopus
dc.subjectAssociation Rules
dc.subjectAssociative Classifier
dc.subjectClassification
dc.subjectInterestingness Measures
dc.subjectRanking
dc.titleObjective measures ensemble in associative classifiers
dc.typeActas de congresos


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