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dc.contributor.authorBouyssou, Denis*
dc.contributor.authorCouceiro, Miguel*
dc.contributor.authorLabreuche, Christophe*
dc.contributor.authorMarichal, Jean-Luc*
dc.contributor.authorMayag, Brice*
dc.date.accessioned2013-03-07T09:40:51Z
dc.date.available2013-03-07T09:40:51Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11090
dc.language.isoenen
dc.subjectChoquet, Théorie de
dc.subjectMachine learning
dc.subjectMultiple criteria decision making
dc.subject.ddc003en
dc.titleUsing Choquet integral in Machine learning: What can MCDA bring?
dc.typeCommunication / Conférence
dc.description.abstractenIn this paper we discuss the Choquet integral model in the realm of Preference Learning, and point out advantages of learning simultaneously partial utility functions and capacities rather than sequentially, i.e., first utility functions and then capacities or vice-versa. Moreover, we present possible interpretation s of the Choquet integral model in Preference Learning based on Shapley values and interaction indices.
dc.identifier.citationpages41-47
dc.relation.ispartoftitleDA2PL 2012 proceedings
dc.relation.ispartofeditorMarc Pirlot, Vincent Mousseau
dc.relation.ispartofpublnameUniversité de Mons
dc.relation.ispartofpublcityMons
dc.relation.ispartofdate2012
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.conftitleDA2PL'2012
dc.relation.confdate2012-11
dc.relation.confcityMons
dc.relation.confcountryBelgium
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2017-04-18T08:06:47Z
hal.person.labIds989*
hal.person.labIds188786*
hal.person.labIds19045*
hal.person.labIds188786*
hal.person.labIds989*
hal.identifierhal-01509532*


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