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

Bouyssou, Denis; Couceiro, Miguel; Labreuche, Christophe; Marichal, Jean-Luc; Mayag, Brice (2012), Using Choquet integral in Machine learning: What can MCDA bring?, in Marc Pirlot, Vincent Mousseau, DA2PL 2012 proceedings, Université de Mons : Mons, p. 41-47

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Type
Communication / Conférence
Date
2012
Conference title
DA2PL'2012
Conference date
2012-11
Conference city
Mons
Conference country
Belgium
Book title
DA2PL 2012 proceedings
Book author
Marc Pirlot, Vincent Mousseau
Publisher
Université de Mons
Published in
Mons
Pages
41-47
Metadata
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Author(s)
Bouyssou, Denis cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Couceiro, Miguel
Faculty of Science, Technology and Communication [Luxembourg] [FSTC]
Labreuche, Christophe
Thales Research and Technology [Palaiseau]
Marichal, Jean-Luc
Faculty of Science, Technology and Communication [Luxembourg] [FSTC]
Mayag, Brice
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
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.
Subjects / Keywords
Choquet, Théorie de; Machine learning; Multiple criteria decision making