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Using Choquet integral in Machine learning: What can MCDA bring?

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DA2PL_Choquet_Paper_Bouyssou_et_Al.pdf (144.0Kb)
Date
2012
Dewey
Recherche opérationnelle
Sujet
Multiple criteria decision making; Choquet; Théorie de; Machine learning
Conference name
DA2PL' 2012 - from Multiple Criteria Decision Aid to Preference Learning
Conference date
11-2012
Conference city
Mons
Conference country
Belgium
URI
https://basepub.dauphine.fr/handle/123456789/20849
Collections
  • LAMSADE : Publications
Metadata
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Author
Bouyssou, Denis
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Couceiro, Miguel
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Labreuche, Christophe
Marichal, Jean-Luc
Mayag, Brice
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Abstract (EN)
In this paper we discuss the Choquet integral model in the realm of Preference Learning, and point out advantages of learning simultaneously partial utility functions and capacities rather than sequentially, i.e., first utility functions and then capacities or vice-versa. Moreover, we present possible interpretation s of the Choquet integral model in Preference Learning based on Shapley values and interaction indices.

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