Abstract
Although encouraging results have been reported, existing Bayesian network (BN) learning algorithms have some troubles on limited data. A statistical or information theoretical measure or a score function may be unreliable on limited datasets, which affects learning accuracy. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm applies Max Relevance and Min Redundancy feature selection technique and proposes Local Bayesian Increment (LBI) function according to the Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that MRMRG algorithm has much better accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.
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References
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search, 2nd edn. MIT Press, Massachusetts (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 Constructing Bayesian Belief Networks from Databases. In: Ambrosio, B., Smets, P. (eds.) UAI ’91. Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence, pp. 86–94. Morgan Kaufmann, San Francisco, CA (1991)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian Networks: the Combination of Knowledge and Statistical Data. Machine Learning 20(3), 197–243 (1995)
Peng, H.C., Ding, C., Long, F.H.: Minimum redundancy maximum relevance feature selection. IEEE Intelligent Systems 20(6), 70–71 (2005)
Peng, H.C., Long, F.H., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on PAMI 27(8), 1226–1238 (2005)
Teyssier, M., Koller, D.: Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks. In: Chickering, M., Bacchus, F., Jaakkola, T. (eds.) UAI ’05. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, pp. 584–590. Morgan Kaufmann, San Francisco, CA (2005)
Moore, D.S.: Goodness-of-Fit Techniques, 1st edn. Marcel Dekker, New York (1986)
Steck, H.: On the Use of Skeletons when Learning in Bayesian Networks. In: Boutilier, C., Goldszmidt, M. (eds.) UAI ’00. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 558–565. Morgan Kaufmann, San Francisco,CA (2000)
Ioannis, T., Laura, E.B.: The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm. Machine Learning 65(1), 31–78 (2006)
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Liu, F., Tian, F., Zhu, Q. (2007). An Improved Greedy Bayesian Network Learning Algorithm on Limited Data. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_6
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DOI: https://doi.org/10.1007/978-3-540-74690-4_6
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