Learning bayesian networks structures from incomplete data: an efficient approach based on extended evolutionary programming

Authors:
Xiaolin Li;Xiangdong He;Senmiao Yuan
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China;Vanda Group, VAS of China Operations, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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) metric. 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. Aiming at preventing and overcoming premature convergence, the algorithm combines the restart strategy into EP. The experimental results illustrate that our algorithm can learn a good structure from incomplete data.