Improving algorithms for structure learning in Bayesian Networks using a new implicit score
Expert Systems with Applications: An International Journal
MICHO: a scalable constraint-based algorithm for learning Bayesian networks
Proceedings of the 2010 ACM Symposium on Applied Computing
Data Mining and Knowledge Discovery
Modeling and estimation of travel behaviors using bayesian network
Intelligent Decision Technologies - Special issue on design of intelligent environment
Grammar-guided evolutionary construction of bayesian networks
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Multimedia Tools and Applications
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
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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.