Online learning of acyclic conditional preference networks from noisy data

Fabien Labernia (1, 2) , Bruno Zanuttini (3) , Brice Mayag (1) , Florian Yger (1) , Jamal Atif (1)

1 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision
2 MILES - Machine Intelligence and Learning Systems
3 Equipe MAD - Laboratoire GREYC - UMR6072

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.

Domaines: Intelligence artificielle [cs.AI]

Fabien Labernia, Bruno Zanuttini, Brice Mayag, Florian Yger, Jamal Atif. Online learning of acyclic conditional preference networks from noisy data. 17th IEEE International Conference on Data Mining (ICDM 2017), Nov 2017, New Orleans, United States. ⟨hal-01619969⟩