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Interactive Elicitation for a Majority Sorting Model with Maximum Margin optimization

Nefla, Ons; Ozturk, Meltem; Viappiani, Paolo; Brigui, Imene (2019), Interactive Elicitation for a Majority Sorting Model with Maximum Margin optimization, in Pekeč, Saša; Venable, Kristen Brent, Algorithmic Decision Theory - 6th International Conference (ADT 2019), Springer : Cham, p. 141-157. 10.1007/978-3-030-31489-7_10

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ADT2019_preprint.pdf (425.6Kb)
Type
Communication / Conférence
Date
2019
Conference title
6th International Conference on Algorithmic Decision Theory (ADT 2019)
Conference date
2019-10
Conference city
Durham, NC
Conference country
United States
Book title
Algorithmic Decision Theory - 6th International Conference (ADT 2019)
Book author
Pekeč, Saša; Venable, Kristen Brent
Publisher
Springer
Published in
Cham
ISBN
978-3-030-31488-0
Number of pages
181
Pages
141-157
Publication identifier
10.1007/978-3-030-31489-7_10
Metadata
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Author(s)
Nefla, Ons

Ozturk, Meltem
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Viappiani, Paolo cc
Laboratoire d'Informatique de Paris 6 [LIP6]
Brigui, Imene
autre
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.
Subjects / Keywords
preference elicitation; ordinal classification; incremental elicitation; MR-sort; simulations

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