Abstract
Learning a General Bayesian Network (GBN) depends on node ordering and correlation of nodes. The current methods are not efficient enough because of the random node ordering and not considering about the degree of independence between nodes. In this paper, we propose a new method for structure learning. It introduces the degree of independence between nodes into the scoring function, and uses genetic algorithm to search for the best ordering. With the improved scoring function and optimal ordering, the Bayesian Network for the Alert System of Industrial Boiler Security is structurally learned, and errors are compared. The experiment results show that our method is more efficient.
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References
Cooper, G., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 309–347 (September 1992)
Heckerman, D.E.: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Machine Learning 20, 197–243 (1995)
Causation, S.P.: Prediction and Search. MIT Press, Cambridge (2000)
Devore, J.L.: Probability and Statistics for engineering and the science, 5th edn., pp. 616–648. Higher Education Press (2004)
Lauritzen, S.L., Spiegelhalter, D.J.: Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems. Journal of the Royal Statistical Society 50(2), 157–224 (1988)
Chen, X., Anantha, G., Lin, X.: Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm. IEEE Transactions on Knowledge and Data Engineering 20(5), 1–13 (2008)
Qin, L., Hao, Z., Chen, Z., Wa, S.J., Yu, J., Xue, S., Mo, J.: pp. 241–346. National Defense Industry Press (2010)
Zhang, L., Guo, H., Ye, B., Wang, S., Lun, Y.: pp. 143–188. Science Press, Beijing (2006)
Peng, H.C., Ding, C.: Structure Search and Stability Enhancement of Bayesian Networks. In: Proc. of the 3rd IEEE International Conference on Data Mining, Melbourne, USA, pp. 621–624 (2003)
Tsamardinos: The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm. Machine Learning 65(1), 31–79 (2006)
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© 2011 Springer-Verlag Berlin Heidelberg
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Yang, L., Liao, Q. (2011). Improving Bayesian Network Structure Learning with Optimized Node Ordering. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23226-8_42
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DOI: https://doi.org/10.1007/978-3-642-23226-8_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23225-1
Online ISBN: 978-3-642-23226-8
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