Learning Bayesian Networks Structures Based on Memory Binary Particle Swarm Optimization

  • Xiao-Lin Li
  • Shuang-Cheng Wang
  • Xiang-Dong He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


This paper describes a new data mining algorithm to learn Bayesian networks structures based on memory binary particle swarm optimization method and the Minimum Description Length (MDL) principle. An memory binary particle swarm optimization (MBPSO) is proposed. A memory influence is added to a binary particle swarm optimization. The purpose of the added memory feature is to prevent and overcome premature convergence by providing particle specific alternate target points to be used at times instead of the best current position of the particle. In addition, our algorithm, like some previous work, does not need to have a complete variable ordering as input. The experimental results illustrate that our algorithm not only improves the quality of the solutions, but also reduces the time cost.


Particle Swarm Optimization Bayesian Network Premature Convergence Binary Particle Swarm Optimization Bayesian Network Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Suzuki, J.: A construction of Bayesian networks from databases based on a MDL scheme. In: Proceedings of the 9th Conference of Uncertainty in Artificial Intelligence, pp. 266–273. Morgan Kaufmann, San Mateo (1993)Google Scholar
  2. 2.
    Xiang, Y., Wong, S.K.M.: Learning conditional independence relations from a probabilistic model, Department of Computer Science, University of Regina, CA, Tech Rep: CS-94-03 (1994)Google Scholar
  3. 3.
    Heckerman, D.: Learning Bayesian network: The combination of knowledge and statistic data. Machine Learning 20, 197–243 (1995)MATHGoogle Scholar
  4. 4.
    Cheng, J., Greiner, R., Kelly, J.: Learning Bayesian networks from data: An efficient algorithm based on information theory. Artificial Intelligence 137, 43–90 (2002)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Lam, W., Bacchus, F.: Learning Bayesian belief networks: An algorithm based on the MDL principle. Computational Intelligence, 10 (1994)Google Scholar
  6. 6.
    Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R., Kuijpers, C.: Structure Learning of Bayesian Network by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Trans. Pattern Analysis and Machine Intelligence 18, 912–926 (1996)CrossRefGoogle Scholar
  7. 7.
    Lam, W., Bacchus, F.: Learning Bayesian belief networks: an algorithm based on the MDL principle. Computational Intelligence 10, 269–293 (1994)CrossRefGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE International Conference of Evolutionary Computation, Anchorage, Alaska, May 1998, pp. 69–73 (1998)Google Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm optimization algorithm. In: Proceedings of the Conference on Systems, Man, and Cybernetics, pp. 4104–4109 (1997)Google Scholar
  11. 11.
    Hendtlass, T.: Preserving Diversity in Particle Swarm Optimization. In: Chung, P.W.H., Hinde, C.J., Ali, M. (eds.) IEA/AIE 2003. LNCS, vol. 2718, pp. 31–40. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiao-Lin Li
    • 1
  • Shuang-Cheng Wang
    • 2
    • 3
  • Xiang-Dong He
    • 4
  1. 1.National Laboratory for Novel Software Technology Nanjing UniversityNanjingChina
  2. 2.Department of Information ScienceShanghai Lixin University of CommerceShanghaiChina
  3. 3.China Lixin Risk Management Research InstituteShanghai Lixin University of CommerceShanghaiChina
  4. 4.Nanjing Research & Development Center ZTE CorporationNanjingChina

Personalised recommendations