An ensemble approach of dual base learners for multi-class classification problems

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Show simple item record Sesmero Lorente, María Paz Alonso Weber, Juan Manuel Gutiérrez Sánchez, Germán Ledezma Espino, Agapito Ismael Sanchis de Miguel, María Araceli 2021-02-08T08:18:12Z 2021-02-08T08:18:12Z 2015-07-01
dc.identifier.bibliographicCitation Sesmero, M.P., Alonso-Weber, J.M., Gutiérrez, G., Ledezma, A., Sanchís, A. (2015). An ensemble approach of dual base learners for multi-class classification problems. Information Fusion, 24, pp. 122-136.
dc.identifier.issn 1566-2535
dc.description.abstract In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient. (C) 2014 Elsevier B.V. All rights reserved.
dc.description.sponsorship This research was supported by the Spanish MICINN under Projects TRA2010-20225-C03-01, TRA 2011-29454-C03-02, and TRA 2011-29454-C03-03.
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2014 Elsevier B.V. All rights reserved. Atribución-Nocomercial-Sinderivadas 3.0 España
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Feature-Selection
dc.subject.other Neural-Networks
dc.subject.other Pattern-Classification
dc.subject.other Stacked Generalization
dc.subject.other Learning Algorithms
dc.subject.other Mutual Information
dc.subject.other Classifiers
dc.subject.other Accuracy
dc.subject.other Rules
dc.title An ensemble approach of dual base learners for multi-class classification problems
dc.type article
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TRA2010-20225-C03-01
dc.relation.projectID Gobierno de España. TRA 2011-29454-C03-02
dc.relation.projectID Gobierno de España. TRA 2011-29454-C03-03
dc.type.version submittedVersion
dc.identifier.publicationfirstpage 122
dc.identifier.publicationlastpage 136
dc.identifier.publicationtitle Information Fusion
dc.identifier.publicationvolume 24
dc.identifier.uxxi AR/0000016568
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