
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
Type
Communication / ConférenceDate
2012Conference title
DA2PL' 2012 - from Multiple Criteria Decision Aid to Preference LearningConference date
2012-11Conference city
MonsConference country
BelgiumMetadata
Show full item recordAuthor(s)
Bouyssou, Denis
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 learningRelated items
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