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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?, DA2PL' 2012 - from Multiple Criteria Decision Aid to Preference Learning, 2012-11, Mons, Belgium

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Type
Communication / Conférence
Date
2012
Conference title
DA2PL' 2012 - from Multiple Criteria Decision Aid to Preference Learning
Conference date
2012-11
Conference city
Mons
Conference country
Belgium
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
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Labreuche, Christophe

Marichal, Jean-Luc

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
Multiple criteria decision making; Choquet; Théorie de; Machine learning