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.
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References
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)
Cheng, J., Greiner, R.: Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence 137, 43–90 (2002)
Cheng, J., Greiner, R.: Compared Bayesian Network classifiers. In: Proceedings of the 15th Conference on UAI (1999)
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)
Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42, 395–405 (1990)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Herskovits, E.H.: Computer-based probabilistic-network construction. Ph.D Thesis, Stanford University (1991)
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)
Koller, D., Sahami, M.: Toward optimal feature selection. In: Proceedings of International Conference on Machine Learning, pp. 284–292 (1996)
Margaritis, D., Thrun, S.: Bayesian network induction via local neighborhoods. In: Proceedings of NIPS (1999)
Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Francisco (1988)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. Lecture Notes in Statistics. Springer, Heidelberg (1993)
Spirtes, P., Glymour, C.: An algorithm for Fast Recovery of Sparse Casual Graphs. Philosophy Methodology Logic (1990)
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)
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)
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)
Yaramakala, S., Margaritis, D.: Speculative Markov blanket discovery for optimal feature selection. In: Proceedings of IEEE International Conference on Data Mining (ICDM) (2005)
Moore, A., Lee, M.S.: Cached sufficient statistics for efficient machine learning with large datasets. Journal of Artificial Intelligence Research 8, 67–91 (1998)
Komarek, P., Moore, A.: A dynamic adaptation of AD-trees for efficient machine learning on large data sets. In: Proceedings of ICML (2000)
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Fu, S., Desmarais, M.C. (2008). Fast Markov Blanket Discovery Algorithm Via Local Learning within Single Pass. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_10
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DOI: https://doi.org/10.1007/978-3-540-68825-9_10
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