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

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Date
2012
Dewey
Recherche opérationnelle
Sujet
Choquet, Théorie de; Machine learning; Multiple criteria decision making
Conference name
DA2PL'2012
Conference date
11-2012
Conference city
Mons
Conference country
Belgium
Book title
DA2PL 2012 proceedings
Author
Marc Pirlot, Vincent Mousseau
Publisher
Université de Mons
Publisher city
Mons
Year
2012
URI
https://basepub.dauphine.fr/handle/123456789/11090
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Author
Bouyssou, Denis
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Couceiro, Miguel
188786 Faculty of Science, Technology and Communication [Luxembourg] [FSTC]
Labreuche, Christophe
19045 Thales Research and Technology [Palaiseau]
Marichal, Jean-Luc
188786 Faculty of Science, Technology and Communication [Luxembourg] [FSTC]
Mayag, Brice
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
41-47
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.