Abstract
This paper presents an automatic Bayesian network construction algorithm where association analysis is employed to guide the construction of network structure. The proposed method is studied in context of data imputation together with a previously proposed technique for automatic Bayesian network construction, Backpropagation neural networks, and two traditional data imputation techniques. The results show that the proposed method performs better or at least as well as does the best of other methods in 84.62% of the cases.
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References
Acid, S., De Campos, L.M.: A Hybrid Methodology for Learning Belief Networks. Benedict International Journal of Approximate Reasoning 1910, 309–315 (2001)
Buntine, W.: Theory Refinement of Bayesian Networks. In: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, pp. 52–60 (1991)
Cheng, J.: Bayesian Belief Network Software [Computer Program](2001), Available: http://www.cs.ualberta.ca/~jcheng/bnpc.htm
Cheng, J., Bell, D.A., Liu, W.: An algorithm for Bayesian belief network construction from data. In: Proc. 6th International Workshop on Artificial Intelligence and Statistics (1997)
Cooper, G.F., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9, 309–348 (1992)
Dash, D., Druzdzel, M.: A Hybrid Anytime Algorithm for the Construction of Causal Models from Sparse Data. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 142–194 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience Publication, New York (2000)
De Campos, L.M.: Independency Relationships and Learning Algorithms for Singly Connected Networks. Journal of Experimental and Theoretical Artificial Intelligence 10, 511–549 (1998)
Friedman, N., Koller, D.: Learning Bayesian Networks from data (2001), [Online] Available: http://www.cs.huji.ac.il/~nirf/Nips01-Tutorial/Nips-tutorial.pdf
Glesner, S., Koller, D.: Constructing Flexible Dynamic Belief Networks from First-Order Probabilistic Knowledge Bases. In: Froidevaux, C., Kohlas, J. (eds.) ECSQARU 1995. LNCS, vol. 946, pp. 217–226. Springer, Heidelberg (1995)
Haddawy, P., Krieger, R.A.: R.A. Principled construction of minimal bayesian net-works from probability logic knowledge bases. Journal of AI Research (submitted)
Haddawy, P.: Generating Bayesian Networks from Probability Logic Knowledge Bases. In: Proc. Tenth Conference on Uncertainty in Artificial Intelligence (UAI 1994), pp. 262–269. Morgan Kaufmann, San Francisco (1994)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann publishers, San Francisco (2001)
Helsper, E.M., van der. Gaag, L.C., Feelders, A.J., Loeffen, W.L.A., Geenen, P.L., Elbers, A.R.W.: Bringing order into Bayesian-network construction. In: Proc. 3rd Interna-tional Conference on Knowledge Capture, pp. 121–128. ACM Press, New York (2005)
Herskovits, E.: Computer Based Probabilistic Networks Construction, Ph.D. thesis, Medical Information Sciences, Stanford University (1991)
Huete, J.F., De Campos, L.M.: Learning Causal Polytrees. In: Moral, S., Kruse, R., Clarke, E. (eds.) ECSQARU 1993. LNCS, vol. 747, pp. 180–185. Springer, Heidelberg (1993)
Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, New York (2001)
Pearl, J.: Probabilistic Reasoning in Intelligence Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Murphy, K.: A Brief Introduction to Graphical Models and Bayesian Networks. (1998), [Online]. Available: http://www.ai.mit.edu/~murphyk/Bayes/bayes.html
Murray, C.: Bayesian Belief Networks [Online]. Available: http://www.murrayc.com/learning/AI/bbn.shtml
Singh, M., Valtorta, M.: An Algorithm for the Construction of Bayesian Network Struc-tures from Data. In: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pp. 259–265 (1993)
Spirtes, P., Meek, C.: Learning Bayesian Networks with Discrete Variables in Data. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 294–299 (1995)
Storkey, A.: Tutorial: Introduction to Belief Networks (2003), [Online] Available http://www.anc.ed.uk/amos/belief.html
Suzuki, A Construction of Bayesian Networks from Databases Based on the MDL Principle. In: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pp. 266–273, 25 (1993)
UCI KDD Archive (2006). Available: http://kdd.ics.uci.edu/
Wermuth, N., Lauritzen, S.: Graphical and Recursive Models for Contingence Tables. Biometrika 72, 537–552 (1983)
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Sornil, O., Poonvutthikul, S. (2006). Constructing Bayesian Networks from Association Analysis. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_26
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DOI: https://doi.org/10.1007/978-3-540-36668-3_26
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