A new boosting design of Support Vector Machine classifiers

Mayhua Lopez, Efrain Tito; Gómez Verdejo, Vanessa; Figueiras, Aníbal

Publication:
A new boosting design of Support Vector Machine classifiers

dc.affiliation.dpto	UC3M. Departamento de Teoría de la Señal y Comunicaciones	es
dc.contributor.author	Mayhua Lopez, Efrain Tito
dc.contributor.author	Gómez Verdejo, Vanessa
dc.contributor.author	Figueiras, Aníbal
dc.contributor.funder	Ministerio de Ciencia e Innovación (España)	es
dc.date.accessioned	2022-01-10T11:53:11Z
dc.date.available	2022-01-10T11:53:11Z
dc.date.issued	2015-09
dc.description.abstract	Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the resulting ensemble can be easily compacted into a monolithic architecture by simply combining the weights that correspond to the same kernels when they appear in different learners, avoiding to increase the operation computational effort for the above potential advantage. This way, the performance advantage that boosting provides can be obtained for monolithic SVMs, i.e., without paying in classification computational effort because many learners are needed. However, SVMs are both stable and strong, and their use for boosting requires to unstabilize and to weaken them. Yet previous attempts in this direction show a moderate success. In this paper, we propose a combination of a new and appropriately designed subsampling process and an SVM algorithm which permits sparsity control to solve the difficulties in boosting SVMs for obtaining improved performance designs. Experimental results support the effectiveness of the approach, not only in performance, but also in compactness of the resulting classifiers, as well as that combining both design ideas is needed to arrive to these advantageous designs.	en
dc.description.sponsorship	This work was supported in part by the Spanish MICINN under Grants TEC 2011-22480 and TIN 2011-24533.	en
dc.format.extent	9
dc.identifier.bibliographicCitation	Mayhua-López, E., Gómez-Verdejo, V. & Figueiras-Vidal, A. R. (2015). A new boosting design of Support Vector Machine classifiers. Information Fusion, 25, 63–71.	en
dc.identifier.doi	https://doi.org/10.1016/j.inffus.2014.10.005
dc.identifier.issn	1566-2535
dc.identifier.publicationfirstpage	63
dc.identifier.publicationlastpage	71
dc.identifier.publicationtitle	Information Fusion	en
dc.identifier.publicationvolume	25
dc.identifier.uri	https://hdl.handle.net/10016/33851
dc.identifier.uxxi	AR/0000016706
dc.language.iso	eng	en
dc.publisher	Elsevier	en
dc.relation.projectID	Gobierno de España. TEC2011-22480	es
dc.relation.projectID	Gobierno de España. TIN2011-24533	es
dc.rights	© 2014 Elsevier B.V. All rights reserved.	en
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Telecomunicaciones	es
dc.subject.other	Real adaboost	en
dc.subject.other	Subsampling	en
dc.subject.other	Support vector machines	en
dc.subject.other	Linear programming	en
dc.subject.other	Ensemble classifiers	en
dc.title	A new boosting design of Support Vector Machine classifiers	en
dc.type	research article	*
dc.type.hasVersion	AM	*
dspace.entity.type	Publication