Using junction trees for structural learning of Bayesian networks

作者：Zhu, Mingmin^*; Liu, Sanyang; Yang, Youlong; Liu, Kui

来源：Journal of Systems Engineering and Electronics, 2012, 23(2): 286-292.

DOI：10.1109/JSEE.2012.00036

• Bayesian network (BN)
• junction tree
• scoring function
• structural learning
• conditional independence

The learning Bayesian network (BN) structure from data is an NP-hard problem and still one of the most exciting challenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.

• 出版日期2012-4
• 单位西安电子科技大学