Journal Article10.1109/TKDE.2007.190732
Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm
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TL;DR: An algorithm that integrates an information-theory-based and a scoring-function-based approach for learning structures of Bayesian networks that can efficiently and accurately identify complex network structures from data is presented.
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Abstract: 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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