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Interactive Elicitation for a Majority Sorting Model with Maximum Margin optimization

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorNefla, Ons
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorOzturk, Meltem
hal.structure.identifierLaboratoire d'Informatique de Paris 6 [LIP6]
dc.contributor.authorViappiani, Paolo
HAL ID: 9572
ORCID: 0000-0002-7922-3877
hal.structure.identifierautre
dc.contributor.authorBrigui, Imene
dc.date.accessioned2020-04-28T10:27:07Z
dc.date.available2020-04-28T10:27:07Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20665
dc.descriptionLe PDF est la version non publiée (preprint).en
dc.language.isoenen
dc.subjectpreference elicitationen
dc.subjectordinal classificationen
dc.subjectincremental elicitationen
dc.subjectMR-sorten
dc.subjectsimulationsen
dc.subject.ddc003en
dc.titleInteractive Elicitation for a Majority Sorting Model with Maximum Margin optimizationen
dc.typeCommunication / Conférence
dc.description.abstractenWe consider the problem of eliciting a model for ordered classification. In particular, we consider Majority Rule Sorting (MR-sort), a popular model for multiple criteria decision analysis, based on pairwise comparisons between alternatives and idealized profiles representing the "limit" of each category. Our interactive elicitation protocol asks, at each step, the decision maker to classify an alternative; these assignments are used as training set for learning the model. Since we wish to limit the cognitive burden of elicitation, we aim at asking informative questions in order to find a good approximation of the optimal classification in a limited number of elicitation steps. We propose efficient strategies for computing the next question and show how its computation can be formulated as a linear program. We present experimental results showing the effectiveness of our approach.en
dc.identifier.citationpages141-157en
dc.relation.ispartoftitleAlgorithmic Decision Theory - 6th International Conference (ADT 2019)en
dc.relation.ispartofeditorPekeč, Saša
dc.relation.ispartofeditorVenable, Kristen Brent
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityChamen
dc.relation.ispartofdate2019
dc.relation.ispartofpages181en
dc.relation.ispartofurl10.1007/978-3-030-31489-7en
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.ispartofisbn978-3-030-31488-0en
dc.relation.conftitle6th International Conference on Algorithmic Decision Theory (ADT 2019)en
dc.relation.confdate2019-10
dc.relation.confcityDurham, NCen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-030-31489-7_10en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-04-28T10:20:42Z
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