Fast Markov Blanket Discovery Algorithm Via Local Learning within Single Pass

  • Shunkai Fu
  • Michel C. Desmarais
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

Abstract

Learning of Markov blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.

Keywords

Markov blanket local learning feature selection single pass  AD-tree 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aliferis, C.F., Tsamardinos, I., Statnikov, A.: HITON, a Novel Markov blanket algorithm for optimal variable selection. In: Proceedings of the 2003 American Medical Informatics Association Annual Symposium, pp. 21–25 (2003)Google Scholar
  2. 2.
    Cheng, J., Greiner, R.: Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence 137, 43–90 (2002)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Cheng, J., Greiner, R.: Compared Bayesian Network classifiers. In: Proceedings of the 15th Conference on UAI (1999)Google Scholar
  4. 4.
    Cheng, J., Bell, D.A., Liu, W.: Learning belief networks from data: An information theory based approach. In: Proceedings of the sixth ACM International Conference on Information and Knowledge Management (1997)Google Scholar
  5. 5.
    Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42, 395–405 (1990)CrossRefGoogle Scholar
  6. 6.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)MATHCrossRefGoogle Scholar
  7. 7.
    Herskovits, E.H.: Computer-based probabilistic-network construction. Ph.D Thesis, Stanford University (1991)Google Scholar
  8. 8.
    Pena, J.M., Nilsson, R., Bjorkegren, J., Tegner, J.: Towards scalable and data efficient learning of Markov boundaries. International Journal of Approximate Reasoning 45(2), 211–232 (2007)MATHCrossRefGoogle Scholar
  9. 9.
    Koller, D., Sahami, M.: Toward optimal feature selection. In: Proceedings of International Conference on Machine Learning, pp. 284–292 (1996)Google Scholar
  10. 10.
    Margaritis, D., Thrun, S.: Bayesian network induction via local neighborhoods. In: Proceedings of NIPS (1999)Google Scholar
  11. 11.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  12. 12.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. Lecture Notes in Statistics. Springer, Heidelberg (1993)MATHGoogle Scholar
  13. 13.
    Spirtes, P., Glymour, C.: An algorithm for Fast Recovery of Sparse Casual Graphs. Philosophy Methodology Logic (1990)Google Scholar
  14. 14.
    Tsamardinos, I., Aliferis, C.F., Statnikov, A.: Time and sample efficient discovery of Markov blankets and direct causal relations. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 673–678 (2003)Google Scholar
  15. 15.
    Tsamardinos, I., Aliferis, C.F.: Towards principled feature selection: Relevancy, filter and wrappers. In: 9th International Workshop on Artificial Intelligence and Statistics (AI&Stats 2003) (2003)Google Scholar
  16. 16.
    Tsamardinos, I., Aliferis, C.F., Stantnikov, A.: Time and sample efficient discovery of Markov blankets and direct causal relations. In: Proceedings of SIGKDD 2003 (2003)Google Scholar
  17. 17.
    Yaramakala, S., Margaritis, D.: Speculative Markov blanket discovery for optimal feature selection. In: Proceedings of IEEE International Conference on Data Mining (ICDM) (2005)Google Scholar
  18. 18.
    Moore, A., Lee, M.S.: Cached sufficient statistics for efficient machine learning with large datasets. Journal of Artificial Intelligence Research 8, 67–91 (1998)MATHMathSciNetGoogle Scholar
  19. 19.
    Komarek, P., Moore, A.: A dynamic adaptation of AD-trees for efficient machine learning on large data sets. In: Proceedings of ICML (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shunkai Fu
    • 1
  • Michel C. Desmarais
    • 1
  1. 1.Ecole Polytechnique de MontrealMontrealCanada

Personalised recommendations