NDLI: Research and application of structure learning algorithm for Bayesian networks from distributed data

Content Provider	IEEE Xplore Digital Library
Author	Shao-Zhong Zhang Hua Ding Xiu-Kun Wang Hong-Bo Liu
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Dalian Univ. of Technol., China (Shao-Zhong Zhang; Hua Ding; Xiu-Kun Wang; Hong-Bo Liu)
Abstract	Bayesian networks have become a popular knowledge representation scheme for probabilistic knowledge. One of the main challenges in Bayesian networks structure learning is the development of inductive learning techniques that scale up to large and possibly physically distributed data sets. We consider an approach to learning the structure of BN from distributed data. This is based on the collective learning strategy, where a local structure is obtained at each site and the global structure is obtained by cross learning and combining. Local learning is used to identify the local structure of local nodes and local links of cross nodes from local sample data sets. Cross learning can find the entire cross-links of cross nodes. Combined together, the local BNs and BN learnt from cross learning and removes any extra local links. We use the collective learning algorithm in distributed flood decision supporting system. The result shows that the Bayesian network structure is the same compare the collective learning algorithm from distributed data sets with learning from concentrated data sets. And meanwhile the collective learning algorithm is more efficient than transmitting all samples to a central site.
Sponsorship	IEEE Systems Man and Cybernetics Tech. Committee on Cybernetics, Hebei Univ. Northwestern Polytechnical Univ
Starting Page	1667
Ending Page	1671
File Size	315093
Page Count	5
File Format	PDF
ISBN	0780381319
DOI	10.1109/ICMLC.2003.1259764
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Knowledge representation Machine learning Computer science Electronic mail Floods Graphical models Expert systems Large-scale systems Database systems
Content Type	Text
Resource Type	Article

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