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Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm

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3 Author(s)
Xue-wen Chen ; Univ. of Kansas, Lawrence ; Anantha, G. ; Xiaotong Lin

Structure learning of Bayesian networks is a well-researched but computationally hard task. We present an algorithm that integrates an information-theory-based approach and a scoring-function-based approach for learning structures of Bayesian networks. Our algorithm also makes use of basic Bayesian network concepts like d-separation and condition independence. We show that the proposed algorithm is capable of handling networks with a large number of variables. We present the applicability of the proposed algorithm on four standard network data sets and also compare its performance and computational efficiency with other standard structure-learning methods. The experimental results show that our method can efficiently and accurately identify complex network structures from data.

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Knowledge and Data Engineering, IEEE Transactions on  (Volume:20 ,  Issue: 5 )