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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 Markov 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 datasets 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.
classification, data mining, Machine learning

X. Chen, X. Lin and G. Anantha, "Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 628-640, 2007.
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