Abstract
Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs from data is helpful to understand the casual relation between the variable. But Learning BNs is a NP hard problem. This paper presents a novel hybrid algorithm for learning Markov Equivalence Classes, which combining dependency analysis and search-scoring approach together. The algorithm uses the constraint to perform a mapping from skeleton to MEC. Experiments show that the search space was constrained efficiently and the computational performance was improved.
Keywords
Supported by NSFC Major Research Program 60496321, National Natural Science Foundation of China under Grant Nos. 60373098, 60573073, 60603030, 60503016 the National High-Tech Research and Development Plan of China under Grant No. 20060110Z2037, the Major Program of Science and Technology Development Plan of Jilin Province under Grant No. 20020303, the Science and Technology Development Plan of Jilin Province under Grant No. 20030523, European Commission under Grant No. TH/Asia Link/010 (111084).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chickering, D.M., Heckerman, D., Meek, C.: Large-sample learning of Bayesian networks is NP-hard. Journal of Machine Learning Research 5, 1287–1330 (2004)
Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.R.: Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence 137, 43–90 (2002)
Thomas, V., Judea, P.: Equivalence and synthesis of causal models. In: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, pp. 255–270. Elsevier Science Inc., Amsterdam (1991)
Verma, T., Pearl, J.: An algorithm for deciding if a set of observed independencies has a causal explanation. In: Dubois, D., Wellman, M.P., D’Ambrosio, B., Smets, P. (eds.) Uncertainty in Artificial Intelligence Proceedings of the Eighth Conference, pp. 323–330. Morgan Kaufman, San Francisco (1992)
Spirtes, P., Glymour, C., Scheines, R.: Causation, prediction, and search. Springer, Heidelberg (1993)
Dor, D., Tarsi, M.: A simple algorithm to construct a consistent extension of a partially oriented graph. Cognitive Systems Laboratory, UCLA, Computer Science Department (1992)
Chickering, D.M.: Learning Equivalence Classes of Bayesian-Network Structure. Journal of Machine Learning Research 2, 445–498 (2002)
Barbosa, V.C., Szwarcfiter, J.L.: Generating all the acyclic orientations of an undirected graph. Information Processing Letters 72, 71–74 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jia, H., Liu, D., Chen, J., Liu, X. (2007). A Hybrid Approach for Learning Markov Equivalence Classes of Bayesian Network. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_67
Download citation
DOI: https://doi.org/10.1007/978-3-540-76719-0_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76718-3
Online ISBN: 978-3-540-76719-0
eBook Packages: Computer ScienceComputer Science (R0)