NDLI: Evolutionary Aggregation and Refinement of Bayesian Networks

Content Provider	IEEE Xplore Digital Library
Author	Kyung-Joong Kim Sung-Bae Cho
Copyright Year	2006
Description	Author affiliation: Yonsei Univ., Seoul (Kyung-Joong Kim; Sung-Bae Cho)
Abstract	Bayesian network (BN) is a useful tool to represent joint probability distribution in the form of graphical model providing flexible inference and uncertainty handling. If there is enough knowledge about domain, it is possible to design the structure and parameters of BN by expert. Also, it can be learned from massive dataset with statistical learning algorithm. Usually, because the search space of Bayesian networks is relatively huge compared to the other models, evolutionary algorithms have been used to find optimal structure and parameters by many researchers. In this paper, we have focused on the topic of adaptation of constructed models for better performance. If there are a number of models constructed or learned by different experts or sources, it is better to fuse them into one model by considering all the information of each model. However, the complexity of the integrated model is relatively higher than previous isolated models. Minimizing the complexity of the integrated model using evolutionary algorithm is proposed. After integrating models into single one, it needs to adapt to the new data from the environment. It is likely to provide wrong results to the newly generated data from the environment and slightly modifying the joint probability distribution is necessary. The refinement process is also guided by the evolutionary algorithm because the space of search is large. Experimental results on a benchmark network show that the proposed adaptation methods with evolutionary algorithm can perform better than heuristics or greedy approaches.
Starting Page	1513
Ending Page	1520
File Size	394086
Page Count	8
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688488
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Evolutionary computation Probability distribution Adaptation model Graphical models Uncertainty Statistical learning Inference algorithms Fuses Humans
Content Type	Text
Resource Type	Article

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