| dc.contributor.author | Nefla, Ons | |
| dc.contributor.author | Ozturk, Meltem | |
| dc.contributor.author | Viappiani, Paolo | |
| dc.contributor.author | Brigui, Imene | |
| dc.date.accessioned | 2020-04-28T10:27:07Z | |
| dc.date.available | 2020-04-28T10:27:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/20665 | |
| dc.description | Le PDF est la version non publiée (preprint). | en |
| dc.language.iso | en | en |
| dc.subject | preference elicitation | en |
| dc.subject | ordinal classification | en |
| dc.subject | incremental elicitation | en |
| dc.subject | MR-sort | en |
| dc.subject | simulations | en |
| dc.subject.ddc | 003 | en |
| dc.title | Interactive Elicitation for a Majority Sorting Model with Maximum Margin optimization | en |
| dc.type | Communication / Conférence | |
| dc.description.abstracten | We 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.citationpages | 141-157 | en |
| dc.relation.ispartoftitle | Algorithmic Decision Theory - 6th International Conference (ADT 2019) | en |
| dc.relation.ispartofeditor | Pekeč, Saša | |
| dc.relation.ispartofeditor | Venable, Kristen Brent | |
| dc.relation.ispartofpublname | Springer | en |
| dc.relation.ispartofpublcity | Cham | en |
| dc.relation.ispartofdate | 2019 | |
| dc.relation.ispartofpages | 181 | en |
| dc.relation.ispartofurl | 10.1007/978-3-030-31489-7 | en |
| dc.subject.ddclabel | Recherche opérationnelle | en |
| dc.relation.ispartofisbn | 978-3-030-31488-0 | en |
| dc.relation.conftitle | 6th International Conference on Algorithmic Decision Theory (ADT 2019) | en |
| dc.relation.confdate | 2019-10 | |
| dc.relation.confcity | Durham, NC | en |
| dc.relation.confcountry | United States | en |
| dc.relation.forthcoming | non | en |
| dc.identifier.doi | 10.1007/978-3-030-31489-7_10 | en |
| dc.description.ssrncandidate | non | en |
| dc.description.halcandidate | non | en |
| dc.description.readership | recherche | en |
| dc.description.audience | International | en |
| dc.relation.Isversionofjnlpeerreviewed | non | en |
| dc.relation.Isversionofjnlpeerreviewed | non | en |
| dc.date.updated | 2020-04-28T10:20:42Z | |
| hal.person.labIds | 989 | |
| hal.person.labIds | 989 | |
| hal.person.labIds | 233 | |
| hal.person.labIds | 115536 | |
