期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2009
卷号：9
期号：11
页码：268-272
出版社：International Journal of Computer Science and Network Security
摘要：Decision making in medical domain often involves incorporating new evidences into existing or working models reflecting the decision problems at hand. We propose a new framework that facilitates effective aggregation of multiple Bayesian Network models. The proposed framework aims to minimize time and effort required to customize and extend the original models through preserving the conditional independence relationships inherent in two or more types of Bayesian network models. We present an algorithm to systematically combine the qualitative and the quantitative parts of the different Bayesian models. Combination of Bayesian models involves integrating both structural and parameters of different models. We also describe how effective the presented algorithm and it can reduce total computational complexity.
关键词：Data privacy; Bayesian networks (BN); privacy-preserving data mining
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