Communication de conférence


Interactive Elicitation of a Majority Rule Sorting Model with Maximum Margin Optimization


in 6th Algorithmic Decision Theory International Conference, Durham, NC, USA, October 25–27, 2019

par Nefla, Ons ; Ozturk, Meltem ; Viappiani, Paolo ; Brigui-Chtioui, Imène (1978-....) ; ADT, Algorithmic Decision Theory International Conference. 6th, Durham, NC, USA, October 25–27, 2019

Édité par Springer 2019 - Ref. 10.1007/978-3-030-31489-7_10 - 141-157 p. - En anglais

ISBN : 978-3-03-031488-0

Résumé

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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