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Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality Sungwon JUNG Kwang Hyung LEE Doheon LEE Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.7 pp.1018-1027 Publication Date: 2007/07/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.7.1018 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Artificial Intelligence and Cognitive Science Keyword: Bayesian network, clustering, order restriction, search space reduction, Full Text: PDF(331.5KB)>>
Summary: We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(«n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available. | ||||||||||
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