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Communication Dans Un Congrès Année : 2022

A Dual Approach for Learning Sparse Representations of Choquet Integrals

Une approche duale pour l'apprentissage de représentations parcimonieuses d'Intégrales de Choquet

Margot Herin
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Patrice Perny

Résumé

Learning simple and explainable preference models able to approximate human preferences is one of the key challenges of multicriteria decision support. In this paper we consider the Choquet integral as a general preference aggregation function and we study the problem of learning an instance, as simple as possible, of this general model to fit a database of preference examples. More precisely, we want to learn a sparse Mobius representation of the capacity defining the Choquet integral. To this end, we study an approach to sparse preference learning based on iterative re-weighted L2 regularization and dualization. We show how to implement this approach and we share the results of numerical tests performed on synthetic preference data. We discuss the benefit of this approach compared to L1 regularization.
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Dates et versions

hal-03978343 , version 1 (08-02-2023)

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  • HAL Id : hal-03978343 , version 1

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Margot Herin, Patrice Perny, Nataliya Sokolovska. A Dual Approach for Learning Sparse Representations of Choquet Integrals. DA2PL From Multiple-Criteria Decision Aid to Preference Learning, Nov 2022, Compiègne, France. ⟨hal-03978343⟩
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