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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.accessioned2020-06-08T14:44:21Z
dc.date.available2020-06-08T14:44:21Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20849
dc.language.isoenen
dc.subjectMultiple criteria decision makingen
dc.subjectChoqueten
dc.subjectThéorie deen
dc.subjectMachine learningen
dc.subject.ddc003en
dc.titleUsing Choquet integral in Machine learning: What can MCDA bring?en
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.en
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.conftitleDA2PL' 2012 - from Multiple Criteria Decision Aid to Preference Learningen
dc.relation.confdate2012-11
dc.relation.confcityMonsen
dc.relation.confcountryBelgiumen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-06-08T14:37:13Z
hal.person.labIds989
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