A new method of learning Bayesian networks structures from incomplete data

Authors:
Xiaolin Li;Xiangdong He;Senmiao Yuan
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China;VAS of China Operations, Vanda Group, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

This paper describes a new data mining algorithm to learn Bayesian networks structures from incomplete data based on extended Evolutionary programming (EP) method and the Minimum Description Length (MDL) principle. This problem is characterized by a huge solution space with a highly multimodal landscape. The algorithm presents fitness function based on expectation, which converts incomplete data to complete data utilizing current best structure of evolutionary process. The algorithm adopts a strategy to alleviate the undulate phenomenon. Aiming at preventing and overcoming premature convergence, the algorithm combines the niche technology into the selection mechanism of EP. In addition, our algorithm, like some previous work, does not need to have a complete variable ordering as input. The experimental results illustrate that our algorithm can learn a good structure from incomplete data.