DocumentCode
256749
Title
A Method for Learning Bayesian Network Structure
Author
Jingnan Li ; Yingxia Zhang
Author_Institution
Sch. of Math. & Stat., Xidian Univ., Xian, China
Volume
2
fYear
2014
fDate
26-27 Aug. 2014
Firstpage
222
Lastpage
225
Abstract
Bayesian network structures from data is an NP-hard problem, In this paper, we propose an approach based on mutual information and PC algorithm methods. This algorithm obain the initial undirected graph using mutual information firstly, obtain a PDAG using PC algorithm. Experimental results show that our method outperforms the PC algorithms under the same conditions, Thus the algorithm decreases the running time and the order of CI tests greatly than the PC algorithm.
Keywords
belief networks; computational complexity; directed graphs; learning (artificial intelligence); Bayesian network structure learning; CI test; NP-hard problem; PC algorithm methods; PDAG; conditional independence; directed acyclic graphs; mutual information; undirected graph; Algorithm design and analysis; Bayes methods; Boolean functions; Data structures; Mutual information; Probabilistic logic; Skeleton; Bayesian network; conditional indepence test; mutual information; structure learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Conference_Location
Hangzhou
Print_ISBN
978-1-4799-4956-4
Type
conf
DOI
10.1109/IHMSC.2014.156
Filename
6911487
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