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dc.creatorCarrizosa Priego, Emilio Josées
dc.date.accessioned2021-04-22T12:38:54Z
dc.date.available2021-04-22T12:38:54Z
dc.date.issued2006-01-01
dc.identifier.citationCarrizosa Priego, E.J. (2006). Deriving Weights in Multiple-Criteria Decision Making with Support Vector Machines. TOP, 14 (2), 399-424.
dc.identifier.issn1134-5764es
dc.identifier.issn1863-8279es
dc.identifier.urihttps://hdl.handle.net/11441/107595
dc.description.abstractA key problem in Multiple-Criteria Decision Making is how to measure the importance of the different criteria when just a partial preference relation among actions is given. In this note we address the problem of constructing a linear score function (and thus how to associate weights of importance to the criteria) when a binary relation comparing actions and partial information (relative importance) on the criteria are given. It is shown that these tasks can be done via Support Vector Machines, an increasingly popular Data Mining technique, which reduces the search of the weights to the resolution of (a series of) nonlinear convex optimization problems with linear constraints. An interactive method is then presented and illustrated by solving a multiple-objective 0-1 knapsack problem. Extensions to the case in which data are imprecise (given by intervals) or intransitivities in strict preferences exist are outlined.es
dc.formatapplication/pdfes
dc.format.extent25 p.es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofTOP, 14 (2), 399-424.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLinear score functionses
dc.subjectsupport vector machineses
dc.subjectmultiple-criteria decision making with partial informationes
dc.subjectdata mininges
dc.titleDeriving Weights in Multiple-Criteria Decision Making with Support Vector Machineses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estadística e Investigación Operativaes
dc.relation.publisherversionhttp://doi.org/10.1007/bf02837570es
dc.identifier.doi10.1007/bf02837570es
dc.contributor.groupUniversidad de Sevilla. FQM329: Optimizaciónes
dc.journaltitleTOPes
dc.publication.volumen14es
dc.publication.issue2es
dc.publication.initialPage399es
dc.publication.endPage424es

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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