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Article Dans Une Revue EURO journal on decision processes Année : 2018

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

Résumé

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.
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Dates et versions

hal-02074081 , version 1 (20-03-2019)

Identifiants

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Fabien Labernia, Florian Yger, Brice Mayag, Jamal Atif. Query-based learning of acyclic conditional preference networks from noisy data. EURO journal on decision processes, 2018, 6 (1-2), ⟨10.1007/s40070-017-0070-3⟩. ⟨hal-02074081⟩
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