Abstract
The use of bayesian networks for knowledge discovery requires learning algorithms which emphasize not only the predictive power but also the structural fidelity of the discovered networks. Previous work on score-based methods for learning equivalence classes of bayesian networks showed that they generally provide better results than classical algorithms, that explore the space of bayesian networks. However, they are considerably slower, mainly because they use more complicated search operators and because they have to build instances of the equivalence classes in order to check their consistency and in order to calculate their score. We propose here a new greedy learning algorithm that explores the space of equivalence classes with a reduced set of operators and realizes the verification of the consistency and the computation of the score without any need for instantiation. We show on five experimental tasks that this algorithm is rather efficient, obtains better scores and discovers structures closer to the “gold-standard” than classical greedy and tabu search in the space of bayesian networks.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. Springer-Verlag, 1993.
N. Friedman and M. Goldszmidt. Learning bayesian networks with local structure.In Proceedings of the Twelfth Intenational Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 1996.
D. Heckerman, D. Geiger, and D.M. Chickering. Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197–243, 1995.
T. Verma and J. Pearl. Equivalence and synthesis of causal models. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence. Elsevier, 1990.
D.M. Chickering. Learning equivalence classes of bayesian-network structures. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 1996
S.A. Andersson, D. Madigan, and M.D. Perlman. A characterization of markov equivalence classes for acyclic digraphs. Annals of Statistics, 25:505–541, 199
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Munteanu, P., Cau, D. (2000). Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_10
Download citation
DOI: https://doi.org/10.1007/3-540-45372-5_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41066-9
Online ISBN: 978-3-540-45372-7
eBook Packages: Springer Book Archive