Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.7 pp.1018-1027
- Publication Date
- 2007/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.7.1018
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence and Cognitive Science
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Sungwon JUNG, Kwang Hyung LEE, Doheon LEE, "Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 7, pp. 1018-1027, July 2007, doi: 10.1093/ietisy/e90-d.7.1018.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.7.1018/_p
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@ARTICLE{e90-d_7_1018,
author={Sungwon JUNG, Kwang Hyung LEE, Doheon LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality},
year={2007},
volume={E90-D},
number={7},
pages={1018-1027},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e90-d.7.1018},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality
T2 - IEICE TRANSACTIONS on Information
SP - 1018
EP - 1027
AU - Sungwon JUNG
AU - Kwang Hyung LEE
AU - Doheon LEE
PY - 2007
DO - 10.1093/ietisy/e90-d.7.1018
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2007
AB - 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.
ER -
English
