Abstract
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects reliability, robustness and accuracy of these algorithms and takes up a large amount of time. To address the above problem, we propose a new Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well-known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency. It is also shown that MRMRG algorithm has better accuracy than most of existing learning algorithms for limited sample datasets.
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References
Heckerman, D.: Bayesian Networks for Data Mining. Microsoft Press, Redmond (1997)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. MIT Press, Cambridge (2000)
Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Belief Networks form Data: An Information Theory Based Approach. Artificial Intelligence 137(1-2), 43–90 (2002)
Cooper, G., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9(4), 309–347 (1992)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian Networks: the Combination of Knowledge and Statistical Data. Machine Learning 20(3), 197–243 (1995)
Suzuki, J.: Learning Bayesian Belief Networks Based on the MDL Principle: An Efficient Algorithm Using the Branch and Bound Technique. In: Proceedings of the Thirteenth International Conference on Machine Learning ICML’1996, pp. 462–470. Morgan Kaufmann, San Francisco (1996)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1998)
Lam, W., Bacchus, F.: Learning Bayesian Belief Networks An approach based on the MDL Principle. Computational Intelligence 10(4), 269–293 (1994)
Peng, H.C., Long, F., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance,and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Moore, D.S.: Goodness-of-Fit Techniques. Marcel Dekker, New York (1986)
Kass, R.E., Raftery, A.E.: Bayes factors. Journal of the American Statistical Association 90(430), 773–796 (1995)
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Liu, F., Zhu, Q. (2007). The Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning Algorithm. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_35
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DOI: https://doi.org/10.1007/978-3-540-73053-8_35
Publisher Name: Springer, Berlin, Heidelberg
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