Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9505)

Abstract

The introduction of temporal dimension makes it difficult and complex to learn dynamic Bayesian network (DBN) structure for huge search space, hence many studies focus on some particular types of DBN, such as dynamic Naive Bayesian Classifier (DNBC). In order to overcome the limited applicability of DBN structure learning methods, this paper proposes an unsupervised evolutionary algorithm in which the selection of initial population has been implemented by means of mutual information to reduce the search space. Furthermore, in view of the poor performance of traditional encoding scheme and the recount of Bayesian information criterion (BIC) score when calculating the individual fitness, we provide a new structure representation without a necessity of the acyclicity test and an updating algorithm for BIC scores with the help of family inheritance to improve the efficiency. Simulations on synthetic data demonstrate that the proposed unsupervised evolutionary algorithm is effective in DBN structure learning.

Keywords

Dynamic Bayesian networks Structure learning Genetic algorithm Mutual information Family BIC score 
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Notes

Acknowledgments

This paper was supported by the International S&T Cooperation Projects of China (2015DFR10510), the National Natural Science Foundation of China (61162010; 61440048; 61562018), and the Natural Science Foundation of Hainan Province, China (614227).

References

  1. 1.
    Heckerman, D.: Bayesian networks for data mining. Data Min. Knowl. Disc. 1, 79–119 (1997)CrossRefGoogle Scholar
  2. 2.
    Zhang, L., Zhang, J., Sun, Y.: The construction and application of Bayesian network in data mining. In: 6th IEEE International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 501–503. IEEE Press, New York (2013)Google Scholar
  3. 3.
    Wachsmuth, S., Brandt-Pook, H., Socher, G., Kummert, F., Sagerer, G.: Multilevel integration of vision and speech understanding using Bayesian networks. In: Christensen, H.I. (ed.) ICVS 1999. LNCS, vol. 1542, pp. 231–254. Springer, Heidelberg (1998) CrossRefGoogle Scholar
  4. 4.
    Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Fisher, D., Lenz, H. (eds.) Learning from Data. LNS, vol. 112, pp. 121–130. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Xia, J., Richard, E.N., Michael, B., Shyam, V.: Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinform. 12, 1–12 (2011)CrossRefGoogle Scholar
  6. 6.
    Liu, Z., Malone, B., Yuan, C.: Empirical evaluation of scoring functions for Bayesian network model selection. BMC Bioinform. S15, 14 (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, S., Xu, G., Du, R.: Restricted Bayesian classification networks. Sc. China Inf. Sci. 56, 1–15 (2013)MathSciNetGoogle Scholar
  8. 8.
    Song, W., Yu, J.X., Cheng, H., Liu, H., He, J., Du, X.: Bayesian network structure learning from attribute uncertain data. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 314–321. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  9. 9.
    Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65, 31–78 (2006)CrossRefGoogle Scholar
  10. 10.
    Wong, M.L., Leung, K.S.: An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach. IEEE Trans. Evol. Comput. 8, 378–404 (2004)CrossRefGoogle Scholar
  11. 11.
    Srinivasa, K.G., Seema, S., Jaiswal, M.: Modelling of time series microarray data using dynamic Bayesian network. Retrovirology 84, 489–492 (2009)Google Scholar
  12. 12.
    Shibata, K., Nakano, H., Miyauchi, A.: A learning method for dynamic Bayesian network structures using a multi-objective particle swarm optimizer. Artif. Life Robot. 16, 329–332 (2011)CrossRefGoogle Scholar
  13. 13.
    Shin, J., Lee, T., Kim, J., Lee, H.: Stochastic model of production and inventory control using dynamic Bayesian network. Artif. Life Robot. 13, 148–154 (2008)CrossRefGoogle Scholar
  14. 14.
    Palacios-Alonso, M.A., Brizuela, C.A., Sucar, L.E.: Evolutionary learning of dynamic Naive Bayesian classifiers. J. Autom. Reason. 45, 21–37 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wu, X., Wen, X., Li, J., Yao, L.: A new dynamic Bayesian network approach for determining effective connectivity from fMRI data. Neural Comput. Appl. 24, 91–97 (2014)CrossRefGoogle Scholar
  16. 16.
    Wei, Z., Xu, H., Li, W., Gui, X., Wu, X.: Improved Bayesian network structure learning with node ordering via K2 algorithm. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS, vol. 8589, pp. 44–55. Springer, Heidelberg (2014) Google Scholar
  17. 17.
    Wang H., Yu K., Yao H.: Learning dynamic Bayesian networks using evolutionary MCMC. In: 2nd IEEE International Conference on Computational Intelligence and Security, pp. 45–50. IEEE Press, Guangzhou (2006)Google Scholar
  18. 18.
    Salama, K.M., Freitas, A.A.: Learning Bayesian network classifiers using ant colony optimization. Swarm Intell. 7, 229–254 (2013)CrossRefGoogle Scholar
  19. 19.
    Li, J., Chen, J.: A hybrid optimization algorithm for Bayesian network structure learning based on database. J. Comput. 9, 2787–2791 (2014)Google Scholar
  20. 20.
    Bac, F.Q., Perov, V.L.: New evolutionary genetic algorithms for NP-complete combinatorial optimization problems. Biol. Cybern. 69, 229–234 (1993)CrossRefGoogle Scholar
  21. 21.
    Lee, J., Chung, W., Kim, E., Kim, S.: A new genetic approach for structure learning of Bayesian networks: matrix genetic algorithm. Int. J. Control Autom. Syst. 8, 398–407 (2010)CrossRefGoogle Scholar
  22. 22.
    Ross, B.J., Zuviria, E.: Evolving dynamic Bayesian networks with multi-objective genetic algorithms. Appl. Intell. 26, 13–23 (2007)CrossRefGoogle Scholar
  23. 23.
    Robinson, R.W.: Counting unlabeled acyclic digraphs. In: Little, C.H.C. (ed.) Combinatorial Mathematics V. LNM, vol. 622, pp. 28–43. Springer, Heidelberg (1977)CrossRefGoogle Scholar
  24. 24.
    Chen, X.W.: Improving Bayesian network structure learning with mutual information-based node ordering in the K2 algorithm. IEEE Trans. Knowl. Data Eng. 20, 628–640 (2007)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.College of Information Science and TechnologyHainan UniversityHaikouChina

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