Enabling large-scale Bayesian network learning by preserving intercluster directionality

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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 c(max)(<< 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.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
2007-07
Language
English
Article Type
Article
Keywords

EXPRESSION DATA; PRINCIPLE

Citation

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E90D, pp.1018 - 1027

ISSN
0916-8532
DOI
10.1093/ietisy/e90-d.7.1018
URI
http://hdl.handle.net/10203/88860
Appears in Collection
BiS-Journal Papers(저널논문)
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