Learning Utilities and Sparse Representations of Capacities for Multicriteria Decision Making with the Bipolar Choquet Integral - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Learning Utilities and Sparse Representations of Capacities for Multicriteria Decision Making with the Bipolar Choquet Integral

Margot Herin
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Patrice Perny

Abstract

This paper concerns preference elicitation and learning of decision models in the context of multicriteria decision making. We propose an approach to learn a compact representation of preferences using a decomposable bi-polar Choquet integral. Our aim is to show how we can successively learn marginal utilities and then sparse representations of capacities involved in the decision model from preference data. We first present a preference elicitation method to learn spline representations of marginal utilities on every point of view. Then we propose a sparse learning approach for determining a compact representation of capacities fitted to the observed preferences. Finally, we present numerical tests showing the advantages of our approach compared to basic methods that do not seek sparsity or that force sparsity a priori by requiring kadditivity. We also apply our approach in the context of preferences over sets.
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Dates and versions

hal-03780464 , version 1 (19-09-2022)

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  • HAL Id : hal-03780464 , version 1

Cite

Margot Herin, Patrice Perny, Nataliya Sokolovska. Learning Utilities and Sparse Representations of Capacities for Multicriteria Decision Making with the Bipolar Choquet Integral. Multidisciplinary Workshop on Advances in Preference Handling, IJCAI, Jul 2022, Vienne, Austria. ⟨hal-03780464⟩
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