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Query-based learning of acyclic conditional preference networks from noisy data

Labernia, Fabien; Yger, Florian; Mayag, Brice; Atif, Jamal (2016), Query-based learning of acyclic conditional preference networks from noisy data, in Busa-Fekete, Róbert; Hüllermeier, Eyke; Mousseau, Vincent; Pfannschmidt, Karlson, From Multiple Criteria Decision Aid to Preference Learning : Proceedings of the DA2PL'2016 EURO Mini Conference, Paderborn University : Paderborn, p. 6

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Type
Communication / Conférence
Date
2016
Conference title
DA2PL'2016 EURO Mini Conference
Conference date
2016-11
Conference city
Paderborn
Conference country
Germany
Book title
From Multiple Criteria Decision Aid to Preference Learning : Proceedings of the DA2PL'2016 EURO Mini Conference
Book author
Busa-Fekete, Róbert; Hüllermeier, Eyke; Mousseau, Vincent; Pfannschmidt, Karlson
Publisher
Paderborn University
Published in
Paderborn
Number of pages
132
Pages
6
Metadata
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Author(s)
Labernia, Fabien
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Yger, Florian cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Mayag, Brice
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Atif, Jamal
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Conditional preference networks (CP-nets) provide a powerful, compact, and intuitive graphical tool to represent the preferences of a user. However learning such a structure is known to be a difficult problem due to its combinatorial nature. We propose in this paper a new, efficient, and robust query-based learning algorithm for acyclic CP-nets. In particular, our algorithm takes into account the incoherences in the user’s preferences or in noisy data by searching in a principled way the variables that condition the other ones. We provide complexity results of the algorithm, and demonstrate its efficiency through an empirical evaluation on synthetic and on real datasets.
Subjects / Keywords
Preference learning; machine learning; artificial intelligence
JEL
C44 - Operations Research; Statistical Decision Theory