Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDM.2017.34 | 2017 IEEE International Conference on Data Mining (ICDM) |
Keywords | Field | DocType |
preference learning,conditional preferences networks,graphical learning,online learning,Hoeffding bound,noisy preferences | Online learning,Recommender system,Hoeffding's inequality,Data mining,Data stream mining,Algorithm design,Noise measurement,Computer science,Artificial intelligence,Asymptotically optimal algorithm,Machine learning,Semantics | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-5386-2449-4 | 0 |
PageRank | References | Authors |
0.34 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabien Labernia | 1 | 1 | 1.40 |
| Bruno Zanuttini | 2 | 289 | 25.43 |
| Brice Mayag | 3 | 33 | 8.36 |
| F. Yger | 4 | 46 | 7.57 |
| Jamal Atif | 5 | 309 | 29.49 |