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http://dx.doi.org/10.5391/JKIIS.2008.18.4.572

Search Space Analysis of R-CORE Method for Bayesian Network Structure Learning and Its Effectiveness on Structural Quality  

Jung, Sung-Won (한국과학기술원 바이오및뇌공학과)
Lee, Do-Heon (한국과학기술원 바이오및뇌공학과)
Lee, Kwang-H. (한국과학기술원 바이오및뇌공학과, AITrc)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.4, 2008 , pp. 572-578 More about this Journal
Abstract
We analyze the search space considered by the previously proposed R-CORE method for learning Bayesian network structures of large scale. Experimental analysis on the search space of the method is also shown. The R-CORE method reduces the search space considered for Bayesian network structures by recursively clustering the random variables and restricting the orders between clusters. We show the R-CORE method has a similar search space with the previous method in the worst case but has a much less search space in the average case. By considering much less search space in the average case, the R-CORE method shows less tendency of overfitting in learning Bayesian network structures compared to the previous method.
Keywords
Bayesian network structure learning; R-CORE; search space analysis;
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