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

Sesmero Lorente, María Paz; Alonso Weber, Juan Manuel; Gutiérrez Sánchez, Germán; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli

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

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)	es
dc.contributor.author	Sesmero Lorente, María Paz
dc.contributor.author	Alonso Weber, Juan Manuel
dc.contributor.author	Gutiérrez Sánchez, Germán
dc.contributor.author	Ledezma Espino, Agapito Ismael
dc.contributor.author	Sanchis de Miguel, María Araceli
dc.date.accessioned	2021-02-08T08:18:12Z
dc.date.available	2021-02-08T08:18:12Z
dc.date.issued	2015-07-01
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.	es
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.	es
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.	es
dc.identifier.doi	https://doi.org/10.1016/j.inffus.2014.09.002
dc.identifier.issn	1566-2535
dc.identifier.publicationfirstpage	122	es
dc.identifier.publicationlastpage	136	es
dc.identifier.publicationtitle	Information Fusion	es
dc.identifier.publicationvolume	24	es
dc.identifier.uri	https://hdl.handle.net/10016/31872
dc.identifier.uxxi	AR/0000016568
dc.language.iso	eng	es
dc.publisher	Elsevier	es
dc.relation.projectID	Gobierno de España. TRA2010-20225-C03-01	es
dc.relation.projectID	Gobierno de España. TRA 2011-29454-C03-02	es
dc.relation.projectID	Gobierno de España. TRA 2011-29454-C03-03	es
dc.rights	© 2014 Elsevier B.V. All rights reserved. Atribución-Nocomercial-Sinderivadas 3.0 España	*
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Informática	es
dc.subject.other	Feature-Selection	es
dc.subject.other	Neural-Networks	es
dc.subject.other	Pattern-Classification	es
dc.subject.other	Stacked Generalization	es
dc.subject.other	Learning Algorithms	es
dc.subject.other	Mutual Information	es
dc.subject.other	Classifiers	es
dc.subject.other	Accuracy	es
dc.subject.other	Rules	es
dc.title	An ensemble approach of dual base learners for multi-class classification problems	es
dc.type	research article	*
dc.type.hasVersion	SMUR	*
dspace.entity.type	Publication