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Interactive Elicitation for a Majority Sorting Model with Maximum Margin optimization

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ADT2019_preprint.pdf (425.6Kb)
Date
2019
Notes
Le PDF est la version non publiée (preprint).
Dewey
Recherche opérationnelle
Sujet
preference elicitation; ordinal classification; incremental elicitation; MR-sort; simulations
DOI
http://dx.doi.org/10.1007/978-3-030-31489-7_10
Conference name
6th International Conference on Algorithmic Decision Theory (ADT 2019)
Conference date
10-2019
Conference city
Durham, NC
Conference country
United States
Book title
Algorithmic Decision Theory - 6th International Conference (ADT 2019)
Author
Pekeč, Saša; Venable, Kristen Brent
Publisher
Springer
Publisher city
Cham
Year
2019
Pages number
181
ISBN
978-3-030-31488-0
Book URL
10.1007/978-3-030-31489-7
URI
https://basepub.dauphine.fr/handle/123456789/20665
Collections
  • LAMSADE : Publications
Metadata
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Author
Nefla, Ons
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ozturk, Meltem
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Viappiani, Paolo
233 Laboratoire d'Informatique de Paris 6 [LIP6]
Brigui, Imene
115536 autre
Type
Communication / Conférence
Item number of pages
141-157
Abstract (EN)
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.

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