Learning Bayesian Networks Through Knowledge Reduction
Translated title
Term
10. Term
Education
Publication year
2007
Submitted on
2012-02-14
Abstract
Learning Bayesian networks from data becomes intractable when a large number of variables are involved in the application domain. Much effort has been made in the past to overcome the computational problem using the divide and conquer strategy. In this Master thesis, it is provided a prior solution to this strategy by introducing a general class of models, named the Bayesian network knots, which explicitly partition the variables into several local components in the network. We propose a learning algorithm called the Overlapping Expansion Learning (OSL) algorithm. Furthermore, we investigate the implications of attribute clustering for learning Bayesian networks. Experimental results show that the OSL is highly competitive. Moreover, we developed a novel attribute clustering algorithm, named the Star Discovery (SD) algorithm. The SD algorithm is able to discover groups of variables with a higher performance than several attribute clustering approaches.
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