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Communication Dans Un Congrès Année : 2016

Dominance based monte carlo algorithm for preference learning in the multi-criteria sorting problem: Theoretical properties

Résumé

In this article we study a new model-free Multi-CriteriaDecision Aiding (MCDA) method for sorting problems where ob-jects are assigned to predefined and ordered categories. The problemthat we deal is the following: given a learning set of objects definedon multi-attributes and already assigned by the decision maker, howto find the assignments of the remaining objects. Being model-free,we do not assume that the decision maker’s reasoning follows somewell-known and explicitly described rules or logic system. We onlyassume that monotonicity should be respected as well as the learningset. The specificity of our approach is to be probabilistic. A MonteCarlo principle is used where the median operator aggregates the re-sults of independent and randomized experiments. We proved thatour final sortings respect the monotonicity and the learning set andthe aggregation with the median operator converges almost surely.
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Dates et versions

hal-01462108 , version 1 (23-02-2017)

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  • HAL Id : hal-01462108 , version 1

Citer

Tom Denat, Meltem Ozturk Escoffier. Dominance based monte carlo algorithm for preference learning in the multi-criteria sorting problem: Theoretical properties. EURO Mini Conference: "From Multiple Criteria Decision Aid to Preference Learning" (DA2PL 2016), Nov 2016, Paderborn, Germany. pp.47-52. ⟨hal-01462108⟩
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