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Online learning of acyclic conditional preference networks from noisy data

Labernia, Fabien; Zanuttini, Bruno; Mayag, Brice; Yger, Florian; Atif, Jamal (2017), Online learning of acyclic conditional preference networks from noisy data, in Karypis, George; Miele, Lucio, Proceedings of the IEEE International Conference on Data Mining (ICDM 2017), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ

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PID5012735.pdf (363.0Kb)
Type
Communication / Conférence
Date
2017
Conference title
17th IEEE International Conference on Data Mining (ICDM 2017)
Conference date
2017-11
Conference city
New Orleans
Conference country
United States
Book title
Proceedings of the IEEE International Conference on Data Mining (ICDM 2017)
Book author
Karypis, George; Miele, Lucio
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
Metadata
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Author(s)
Labernia, Fabien
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Zanuttini, Bruno
Groupe de Recherche en Informatique, Image et Instrumentation de Caen [GREYC]
Mayag, Brice
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]
Atif, Jamal
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
We deal with online learning of acyclic Conditional Preference networks (CP-nets) from data streams, possibly corrupted with noise. We introduce a new, efficient algorithm relying on (i) information-theoretic measures defined over the induced preference rules, which allow us to deal with corrupted data in a principled way, and on (ii) the Hoeffding bound to define an asymptotically optimal decision criterion for selecting the best conditioned variable to update the learned network. This is the first algorithm dealing with online learning of CP-nets in the presence of noise. We provide a thorough theoretical analysis of the algorithm, and demonstrate its effectiveness through an empirical evaluation on synthetic and on real datasets.
Subjects / Keywords
Conditional Preference networks (CP-nets); data mining

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