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.
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).
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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
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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
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