Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks

We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. 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]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[2]  Christopher Meek,et al.  Learning Bayesian Networks with Discrete Variables from Data , 1995, KDD.

[3]  Lawrence J. Fogel,et al.  Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming , 1999 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Wai Lam,et al.  LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..

[6]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  David Heckerman,et al.  Learning Bayesian Networks: Search Methods and Experimental Results , 1995 .

[10]  Gregory F. Cooper,et al.  An Entropy-driven System for Construction of Probabilistic Expert Systems from Databases , 1990, UAI.

[11]  Judea Pearl,et al.  Bayesian Networks , 1998, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[12]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[13]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[14]  Pedro Larrañaga,et al.  Learning Bayesian network structures by searching for the best ordering with genetic algorithms , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[15]  Wai Lam,et al.  Bayesian Network Refinement Via Machine Learning Approach , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[17]  Gregory F. Cooper,et al.  The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks , 1989, AIME.

[18]  Franz von Kutschera,et al.  Causation , 1993, J. Philos. Log..