Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning
Joint Authors
Liu, Guangyi
Li, Ou
Zhang, Dalong
Song, Tao
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-11-25
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues.
Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper.
We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network.
Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.
American Psychological Association (APA)
Liu, Guangyi& Li, Ou& Zhang, Dalong& Song, Tao. 2014. Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1044401
Modern Language Association (MLA)
Liu, Guangyi…[et al.]. Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning. Mathematical Problems in Engineering No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1044401
American Medical Association (AMA)
Liu, Guangyi& Li, Ou& Zhang, Dalong& Song, Tao. Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1044401
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044401