A new boosting design of Support Vector Machine classifiers

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dc.contributor.author Mayhua Lopez, Efrain Tito
dc.contributor.author Gómez Verdejo, Vanessa
dc.contributor.author Figueiras Vidal, Aníbal Ramón
dc.date.accessioned 2022-01-10T11:53:11Z
dc.date.available 2022-01-10T11:53:11Z
dc.date.issued 2015-09
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.
dc.identifier.issn 1566-2535
dc.identifier.uri http://hdl.handle.net/10016/33851
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.
dc.description.sponsorship This work was supported in part by the Spanish MICINN under Grants TEC 2011-22480 and TIN 2011-24533.
dc.format.extent 9
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2014 Elsevier B.V. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Real adaboost
dc.subject.other Subsampling
dc.subject.other Support vector machines
dc.subject.other Linear programming
dc.subject.other Ensemble classifiers
dc.title A new boosting design of Support Vector Machine classifiers
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.inffus.2014.10.005
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2011-22480
dc.relation.projectID Gobierno de España. TIN2011-24533
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 63
dc.identifier.publicationlastpage 71
dc.identifier.publicationtitle Information Fusion
dc.identifier.publicationvolume 25
dc.identifier.uxxi AR/0000016706
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
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