This Article 
 Bibliographic References 
 Add to: 
Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks
February 1999 (vol. 21 no. 2)
pp. 174-178

Abstract—We have developed a new approach (MDLEP) to learning Bayesian network structures based on the Minimum Description Length (MDL) principle and Evolutionary Programming (EP). It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.

[1] I.A. Beinlich, H.J. Suermondt, R.M. Chavez, and G.F. Cooper, "The ALARM Monitoring System: A Case Study With Two Probabilistic Inference Techniques for Belief Networks," Proc. Second European Conf. Artificial Intelligence in Medicine, pp. 247-256, 1989.
[2] D. Chickering, D. Geiger, and D. Heckerman, "Learning Bayesian Networks: Search Methods and Experimental Results," Proc. Fifth Conf. Artificial Intelligence and Statistics, pp. 112-128, 1995.
[3] G.F. Cooper and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, vol. 9, pp. 309–347, 1992.
[4] D. Fogel, "An Introduction to Simulated Evolutionary Optimization," IEEE Trans. Neural Networks, vol. 5, pp. 3-14, Jan. 1994.
[5] L. Fogel, A. Owens, and M. Walsh, Artificial Intelligence Through Simulated Evolution.New York: John Wiley and Sons, 1966.
[6] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley, 1989.
[7] D. Heckerman, D. Geiger, and D.M. Chickering, “Learning Bayesian Networks: The Combination of Knowledge and Statistical Data,” Machine Learning, vol. 20, pp. 197–243, 1995.
[8] D. Heckerman, J. Breese, and K. Rommelse, “Troubleshooting under Uncertainty,” Comm. ACM, vol. 38, no. 3, pp. 27-41, Mar. 1995.
[9] E. Herskovits and G. Cooper, "KUTATO: An Entropy-Driven System for Construction of Probabilistic Expert Systems From Databases," Tech. Rep. KSL-90-22, Knowledge Systems Laboratory, Medical Computer Science, Stanford Univ., 1990.
[10] J.H. Holland, Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, 1975.
[11] W. Lam and F. Bacchus, "Learning Bayesian Belief Networks—An Approach Based on the MDL Principle," Computational Intelligence, vol. 10, no. 3, pp. 269-293, 1994.
[12] W. Lam, “Bayesian Network Refinement via Machine Learning Approach,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 240-251, Mar. 1998.
[13] P. Larrañaga, C. Kuijpers, R. Murga, and Y. Yurramendi, "Learning Bayesian Network Structures by Searching for the Best Ordering With Genetic Algorithms," IEEE Trans. Systems, Man, and Cybernetics—Part A: Systems and Humans, vol. 26, no. 4, pp. 487-493, 1996.
[14] P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, and C. Kuijpers, "Structure Learning of Bayesian Network by Genetic Algorithms: A Performance Analysis of Control Parameters," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 912-926, Sept. 1996.
[15] J. Rissanen, "Modeling by Shortest Data Description," Automatica, vol. 14, pp. 465-471, 1978.
[16] P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search.New York: Springer-Verlag, 1993.
[17] P. Spirtes and C. Meek, "Learning Bayesian Networks With Discrete Variables From Data," Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 294-299, 1995.

Index Terms:
Evolutionary computation, Bayesian networks, unsupervised learning, minimum description length principle, genetic algorithms.
Man Leung Wong, Wai Lam, Kwong Sak Leung, "Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 2, pp. 174-178, Feb. 1999, doi:10.1109/34.748825
Usage of this product signifies your acceptance of the Terms of Use.