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A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

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Abstract

Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time (NP) hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it difficult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo (MCMC) algorithm and propose an algorithm called Constrained MCMC (C-MCMC) algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined: existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution (CPD) of edges and the probability distribution (PD) of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.

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References

  1. Jensen F V. Introduction to Bayesian Networks. Secaucus, USA: Springer-Verlag, 1996.

  2. Chickering D M, Heckerman D, Meek C. Large-sample learning of Bayesian networks is NP-hard. Journal of Machine Learning Research, 2004, 5: 1287-1330.

    MATH  MathSciNet  Google Scholar 

  3. Heckerman D, Geiger D, Chickering D M. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 1995, 20(3): 197-243.

    MATH  Google Scholar 

  4. Chickering D M. Learning equivalence classes of Bayesiannetwork structures. Journal of Machine Learning Research, 2002, 2: 445-498.

    MATH  MathSciNet  Google Scholar 

  5. Pernkopf F, Bilmes J. Efficient heuristics for discriminative structure learning of Bayesian network classifiers. Journal of Machine Learning Research, 2010, 11: 2323–2360.

    MATH  MathSciNet  Google Scholar 

  6. Giudici P, Castelo R. Improving Markov chain Monte Carlo model search for data mining. Machine Learning, 2003, 50(1/2): 127-158.

    Article  MATH  Google Scholar 

  7. Koivisto M, Sood K. Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research, 2004, 5: 549-573.

    MathSciNet  Google Scholar 

  8. Silander T, Myllymaki P. A simple approach for finding the globally optimal Bayesian network structure. In Proc. the 22nd Conference on Uncertainty in Artificial Intelligence, July 2006.

  9. Perrier E, Imoto S, Miyano S. Finding optimal Bayesian network given a super-structure. Journal of Machine Learning Research, 2008, 9: 2251-2286.

    MATH  MathSciNet  Google Scholar 

  10. Kojima K, Perrier E, Imoto S, Miyano S. Optimal search on clustered structural constraint for learning Bayesian network structure. Journal of Machine Learning Research, 2010, 11: 285-310.

    MathSciNet  Google Scholar 

  11. de Campos C P, Ji Q. Efficient structure learning of Bayesian networks using constraints. Journal of Machine Learning Research, 2011, 12: 663-689.

    Google Scholar 

  12. Ehlers R S. Computational tools for comparing asymmetric GARCH models via Bayes factors. Mathematics and Computers in Simulation, 2012, 82(5): 858-867.

    Article  MATH  MathSciNet  Google Scholar 

  13. Grzegorczyk M, Husmeier D. Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Machine Learning, 2008, 71(2/3):265-305.

    Article  Google Scholar 

  14. Corander J, Ekdahl M, Koski T. Parallel interacting MCMC for learning of topologies of graphical models. Data Mining and Knowledge Discovery, 2008, 17(3): 431-456.

    Article  MathSciNet  Google Scholar 

  15. Teyssier M, Koller D. Ordering-based search: A simple and effective algorithm for learning Bayesian networks. In Proc. the 21st Conference on Uncertainty in Artificial Intelligence, June 2005, pp.548-549.

  16. Cano A, Masegosa A R, Moral S. A method for integrating expert knowledge when learning Bayesian networks from data. IEEE Transaction on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41(5): 1382-1394.

    Article  Google Scholar 

  17. de Campos L M, Castellano J G. Bayesian network learning algorithms using structural restrictions. International Journal of Approximate Reasoning, 2007, 45(2): 233-254.

    Article  MATH  MathSciNet  Google Scholar 

  18. Heckerman D, Geiger D, Chickering D M. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 1995, 20(3): 197-243.

    MATH  Google Scholar 

  19. Lauritzen S L, Spiegelhalter D J. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B (Methodological), 1988, 50(2): 157-224.

  20. Binder J, Koller D, Russell S, Kanazawa K. Adaptive probabilistic networks with hidden variables. Machine Learning, 1997, 29(2/3): 213-244.

    Article  Google Scholar 

  21. Beinlich I A, Suermondt H J, Chavez RM, Cooper G F. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proc. the 2nd European Conference on Artificial Intelligence in Medicine, August 1989, pp.247-256.

  22. Forbes J, Huang T, Kanazawa K, Russell S. The BATmobile: Towards a Bayesian automated taxi. In Proc. the 14th International Joint Conference on Artificial Intelligence, August 1995, pp.1878-1885.

  23. Acid S, de Campos L M. Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs. Journal of Artificial Intelligence Research, 2003, 18: 445-490.

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, 101408, China

    Jun-Gang Xu & Yue Zhao

  2. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Jian Chen & Chao Han

Authors
  1. Jun-Gang Xu
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  2. Yue Zhao
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  3. Jian Chen
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  4. Chao Han
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Correspondence to Jun-Gang Xu.

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Xu, JG., Zhao, Y., Chen, J. et al. A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge. J. Comput. Sci. Technol. 30, 713–724 (2015). https://doi.org/10.1007/s11390-015-1556-8

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  • DOI: https://doi.org/10.1007/s11390-015-1556-8

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