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Improved algorithm based on mutual information for learning Bayesian network structures in the space of equivalence classes

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Abstract

As is well known, greedy algorithm is usually used as local optimization method in many heuristic algorithms such as ant colony optimization, taboo search, and genetic algorithms, and it is significant to increase the convergence speed and learning accuracy of greedy search in the space of equivalence classes of Bayesian network structures. An improved algorithm, I-GREEDY-E is presented based on mutual information and conditional independence tests to firstly make a draft about the real network, and then greedily explore the optimal structure in the space of equivalence classes starting from the draft. Numerical experiments show that both the BIC score and structure error have some improvement, and the number of iterations and running time are greatly reduced. Therefore the structure with highest degree of data matching can be relatively faster determined by the improved algorithm.

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Acknowledgments

I would like to thank You-Long Yang, Xiao-Li Gao and Ming-Min Zhu for useful discussions and suggestions. I would also like to thank the anonymous reviewers for their help with improving this paper.

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Authors and Affiliations

  1. Department of Science, Xi’an Electronic and Science University, 710071, Xi’an, China

    Bing Han Li & San Yang Liu

  2. Department of Mechanical Engineering, Xi’an Jiao Tong University, 710049, Xi’an, China

    Zhan Guo Li

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  1. Bing Han Li
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  2. San Yang Liu
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  3. Zhan Guo Li
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Correspondence to Bing Han Li.

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Li, B.H., Liu, S.Y. & Li, Z.G. Improved algorithm based on mutual information for learning Bayesian network structures in the space of equivalence classes. Multimed Tools Appl 60, 129–137 (2012). https://doi.org/10.1007/s11042-011-0801-6

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  • DOI: https://doi.org/10.1007/s11042-011-0801-6

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