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dc.creatorCarrizosa Priego, Emilio Josées
dc.creatorMartín Barragán, Belénes
dc.creatorRomero Morales, María Doloreses
dc.date.accessioned2016-09-08T10:26:18Z
dc.date.available2016-09-08T10:26:18Z
dc.date.issued2008-03
dc.identifier.citationCarrizosa Priego, E.J., Martín Barragán, B. y Romero Morales, M.D. (2008). Multi-group support vector machines with measurement costs a biobjective approach. Discrete Applied Mathematics, 156 (6), 950-966.
dc.identifier.issn0166-218Xes
dc.identifier.urihttp://hdl.handle.net/11441/44825
dc.description.abstractSupport Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modelled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. We introduce a biobjective mixed integer problem, for which Pareto optimal solutions are obtained. Those Pareto optimal solutions correspond to different classification rules, among which the user would choose the one yielding the most appropriate compromise between the cost and the expected misclassification rate.es
dc.description.sponsorshipMinisterio de Ciencia y Tecnologíaes
dc.description.sponsorshipPlan Andaluz de Investigaciónes
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofDiscrete Applied Mathematics, 156 (6), 950-966.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMulti-group classificationes
dc.subjectPareto optimalityes
dc.subjectBiobjective mixed integer programminges
dc.subjectFeature costes
dc.subjectSupport vector machineses
dc.titleMulti-group support vector machines with measurement costs a biobjective approaches
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estadística e Investigación Operativaes
dc.relation.projectIDBFM2002-11282-Ees
dc.relation.projectIDBFM2002-04525-C02-02es
dc.relation.projectIDFQM-329es
dc.relation.publisherversionhttp://ac.els-cdn.com/S0166218X07003861/1-s2.0-S0166218X07003861-main.pdf?_tid=66b6f05e-75ae-11e6-b551-00000aab0f02&acdnat=1473330439_e3be85c511b54f4525901444222a55dfes
dc.identifier.doi10.1016/j.dam.2007.05.060es
dc.contributor.groupUniversidad de Sevilla. FQM329: Optimizaciones
idus.format.extent24 p.es
dc.journaltitleDiscrete Applied Mathematicses
dc.publication.volumen156es
dc.publication.issue6es
dc.publication.initialPage950es
dc.publication.endPage966es
dc.identifier.idushttps://idus.us.es/xmlui/handle/11441/44825
dc.contributor.funderMinisterio de Ciencia y Tecnología (MCYT). España
dc.contributor.funderJunta de Andalucía

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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