Reference : Using Choquet integral in Machine Learning: what can MCDA bring?
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Physical, chemical, mathematical & earth Sciences : Mathematics
Business & economic sciences : Quantitative methods in economics & management
http://hdl.handle.net/10993/9587
Using Choquet integral in Machine Learning: what can MCDA bring?
English
Bouyssou, Denis mailto [University Paris-Dauphine, Paris, France > Lamsade]
Couceiro, Miguel mailto [University Paris-Dauphine, Paris, France > Lamsade]
Labreuche, Christophe mailto [Thales Research & Technology, Palaiseau, France]
Marichal, Jean-Luc mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Mathematics Research Unit >]
Mayag, Brice mailto [University Paris-Dauphine, Paris, France > Lamsade]
2012
DA2PL' 2012 - from Multiple Criteria Decision Aid to Preference Learning
41-47
Yes
No
International
DA2PL Workshop (from Multiple Criteria Decision Aid to Preference Learning)
from 15-11-2012 to 16-11-2012
Marc Pirlot (UMONS) and Vincent Mousseau (ECP)
Mons
Belgium
[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.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/9587
http://www.lgi.ecp.fr/DA2PL

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