BIC-based node order learning for improving Bayesian network structure learning

Lv, Y; Miao, J; Liang, J; Chen, L; Qian, Y

BIC-based node order learning for improving Bayesian network structure learning

Lv, Y Miao, J Liang, J Chen, L

Qian, Y

Permalink

Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Frontiers of Computer Science, 2021, 15, (6), pp. 156337
Issue Date:: 2021-12-01

Closed Access

	Filename	Description	Size
	Lv2021_Article_BIC-basedNodeOrderLearningForI.pdf	Published version	693.84 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lv, Y
dc.contributor.author	Miao, J
dc.contributor.author	Liang, J
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Qian, Y
dc.date.accessioned	2022-05-14T03:26:32Z
dc.date.available	2022-05-14T03:26:32Z
dc.date.issued	2021-12-01
dc.identifier.citation	Frontiers of Computer Science, 2021, 15, (6), pp. 156337
dc.identifier.issn	2095-2228
dc.identifier.issn	2095-2236
dc.identifier.uri	http://hdl.handle.net/10453/157337
dc.description.abstract	Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithm based on the frequently used Bayesian information criterion (BIC) score function. The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective. Specifically, we first find the most dependent node for each individual node, prove analytically that the dependencies are undirected, and then construct undirected subgraphs UG. Secondly, the UG is examined and connected into a single undirected graph UGC. The relation between the subgraph number and the node number is analyzed. Thirdly, we provide the rules of orienting directions for all edges in UGC, which converts it into a directed acyclic graph (DAG). Further, we rank the DAG’s topology order and describe the BIC-based node order learning algorithm. Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples, and in polynomial time with respect to the number of variables. Finally, experimental results demonstrate significant performance improvement by comparing with other methods.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Frontiers of Computer Science
dc.relation.isbasedon	10.1007/s11704-020-0268-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	BIC-based node order learning for improving Bayesian network structure learning
dc.type	Journal Article
utslib.citation.volume	15
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-14T03:26:30Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	6

Abstract:

Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithm based on the frequently used Bayesian information criterion (BIC) score function. The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective. Specifically, we first find the most dependent node for each individual node, prove analytically that the dependencies are undirected, and then construct undirected subgraphs UG. Secondly, the UG is examined and connected into a single undirected graph UGC. The relation between the subgraph number and the node number is analyzed. Thirdly, we provide the rules of orienting directions for all edges in UGC, which converts it into a directed acyclic graph (DAG). Further, we rank the DAG’s topology order and describe the BIC-based node order learning algorithm. Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples, and in polynomial time with respect to the number of variables. Finally, experimental results demonstrate significant performance improvement by comparing with other methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157337