Abstract
Bayesian network is an important and powerful method for representing and reasoning under conditions of uncertainty, and has been widely used in artificial intelligence and knowledge engineering. Structure learning is certainly the most difficult problem in Bayesian network research. In this paper we give an introduction to Bayesian networks, and review the related work on leaning Bayesian networks. Then we discuss the major difficulties in structure learning, and propose an efficient algorithm for cooperative learning of Bayesian network structure from database. The experimental results from a case study prove that such an approach is feasible and robust.
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Pearl, J.: Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29(3), 241–288 (1986)
Beinlich, I.A., Suermondt, H.J., Chavez, R.M., et al.: The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In: Proceedings of the European Conference on Artificial Intelligence in Medicine, pp. 247–256 (1989)
Heckerman, D.: Bayesian Network for data mining. Data mining and knowledge discovery 1, 79–119 (1997)
Kumar, V.P., Desai, U.B.: Image interpretation using Bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(1), 74–77 (1996)
Piater, J.H., Grupen, R.A.: Feature learning for recognition with Bayesian networks. In: The 15th International Conference on Pattern Recognition, vol. 1, pp. 17–20 (2000)
Heping, P., Lin, L.: Fuzzy Bayesian networks - a general formalism for representation, inference and learning with hybrid Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence 14(7), 941–962 (2000)
Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14(3), 462–467 (1968)
Rebane, G., Pearl, J.: The recovery of causal poly-trees from statistical data. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 222–228 (1987)
Cooper, G., Herskovits, E.: A Bayesian method for the induction of Bayesian networks from data. Machine Learning 9, 309–347 (1992)
Buntine, W.: Operations for learning with graphical models. Journal of Artificial Intelligence Research 2, 159–225 (1994)
Heckerman, D., Geiger, D., Chickering, M.D.: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20(2), 197–243 (1995)
Lam, W., Bacchus, F.: Learning Bayesian Belief Networks: An approach based on the MDL Principle. Computational Intelligence 10, 269–293 (1994)
Singh, M., Valtorta, M.: Construction of Bayesian network structures from data: A brief survey and an efficient algorithm. International Journal of Approximate Reasoning 12, 111–131 (1995)
Chickering, D.M., Heckerman, D.: Efficient approximations for the marginal likelihood of incomplete data given a Bayesian networks. Machine Learning 29, 181–212 (1997)
Kwoh, C.K., Gillies, D.F.: Using hidden nodes in Bayesian networks. Artificial Intelligence 88(12), 1–38 (1997)
Nikovski, D.: Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Transactions on Knowledge and Data Engineering 12, 509–516 (2000)
Chickering, D.M.: Learning equivalence classes of Bayesian network structures. Journal of Machine Learning Research 2, 445–498 (2002)
Chickering, D.: Learning equivalence classes of Bayesian network structures. In: Proc. of Twelfth Conference on Uncertainty in Artificial Intelligence, pp. 150–157 (1996)
Kozlowski, D.C., Yamamoto, C.H., Carvalho, F.L.: Using Bayesian networks to build data mining applications for an electronic commerce environment. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, pp. 72–77 (2002)
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, J., Pan, H., Wan, Y. (2005). An Algorithm for Cooperative Learning of Bayesian Network Structure from Data. In: Shen, W., Lin, Z., Barthès, JP.A., Li, T. (eds) Computer Supported Cooperative Work in Design I. CSCWD 2004. Lecture Notes in Computer Science, vol 3168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11568421_9
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DOI: https://doi.org/10.1007/11568421_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29400-9
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