EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA

Ahmad Ahdab, Marc Le Goc

Abstract

This paper addresses the problem of learning a Dynamic Bayesian network from timed data without prior knowledge to the system. One of the main problems of learning a Dynamic Bayesian network is building and orienting the edges of the network avoiding loops. The problem is more difficult when data are timed. This paper proposes an algorithm based on an adequate representation of a set of sequences of timed data and uses an information based measure of the relations between two edges. This algorithm is a part of the Timed Observation Mining for Learning (TOM4L) process that is based on the Theory of the Timed Observations. The paper illustrates the algorithm with an application on the Apache system of the Arcelor-Mittal Steel Group, a real world knowledge based system that diagnoses a galvanization bath.

References

  1. Benayadi, N., Le Goc, M., (2008). Discovering Temporal Knowledge from a Crisscross of Timed Observations. To appear in the proceedings of the 18th European Conference on Artificial Intelligence (ECAI'08), University of Patras, Patras, Greece.
  2. Bouché, P., Le Goc, M., Giambiasi, N., (2005). Modeling discrete event sequences for discovering diagnosis signatures. Proceedings of the Summer Computer Simulation Conference (SCSC05) Philadelphia, USA.
  3. Cheeseman, P., Stutz, J., (1995). Bayesian classification (Auto-Class): Theory and results. Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, p. 153-180.
  4. Cheng, J., Bell, D., Liu, W., (1997). Learning Bayesian Networks from Data An Efficient Approach Based on Information Theory.
  5. Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W., (2002). Learning Bayesian Networks from Data: An Information-Theory Based Approach. Artificial Intelligence, 137, 43-90.
  6. Chickering, D. M., Geiger, D., Heckerman, D., (1994). Learning Bayesian Networks is NP-Hard. Technical Report MSR-TR-94-17, Microsoft Research, Microsoft Corporation.
  7. Cooper, G. F., Herskovits, E., (1992). A Bayesian Method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
  8. Friedman, N., (1998). The Bayesian structural EM algorithm. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, p. 129-138.
  9. Heckerman, D., Geiger, D., Chickering, D. M., (1997). Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning Journal, 20(3).
  10. Le Goc, M., Bouché, P., and Giambiasi, N., (2005). Stochastic modeling of continuous time discrete event sequence for diagnosis. Proceedings of the 16th International Workshop on Principles of Diagnosis (DX'05) Pacific Grove, California, USA.
  11. Le Goc, M., (2006). Notion d'observation pour le diagnostic des processus dynamiques: Application a Sachem et a la découverte de connaissances temporelles. Hdr, Faculté des Sciences et Techniques de Saint Jérôme.
  12. Myers, J., Laskey, K., Levitt, T., (1999). Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms.
  13. Pearl, J., (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, Calif.: Morgan Kaufmann.
Download


Paper Citation


in Harvard Style

Ahdab A. and Le Goc M. (2010). EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 226-231. DOI: 10.5220/0002897802260231


in Bibtex Style

@conference{iceis10,
author={Ahmad Ahdab and Marc Le Goc},
title={EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={226-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002897802260231},
isbn={978-989-8425-05-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA
SN - 978-989-8425-05-8
AU - Ahdab A.
AU - Le Goc M.
PY - 2010
SP - 226
EP - 231
DO - 10.5220/0002897802260231