[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 interpretations of the Choquet integral model in Preference Learning based on Shapley values and interaction indices.
Disciplines :
Mathematics Quantitative methods in economics & management
Author, co-author :
Bouyssou, Denis; University Paris-Dauphine, Paris, France > Lamsade
Couceiro, Miguel; University Paris-Dauphine, Paris, France > Lamsade
Labreuche, Christophe; Thales Research & Technology, Palaiseau, France
Marichal, Jean-Luc ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Mathematics Research Unit
Mayag, Brice; University Paris-Dauphine, Paris, France > Lamsade
Language :
English
Title :
Using Choquet integral in Machine Learning: what can MCDA bring?
Publication date :
2012
Event name :
DA2PL Workshop (from Multiple Criteria Decision Aid to Preference Learning)
Event organizer :
Marc Pirlot (UMONS) and Vincent Mousseau (ECP)
Event place :
Mons, Belgium
Event date :
from 15-11-2012 to 16-11-2012
Audience :
International
Main work title :
DA2PL' 2012 - from Multiple Criteria Decision Aid to Preference Learning
