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Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric

Li, Gang, Tong, F. and Dai, Honghua 2001, Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric, in Proceedings : 2001 IEEE International Conference on Data Mining, 29 November--2 December 2001, San Jose, California, IEEE Computer Society, Los Alamitos, Calif., pp. 615-616.

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Title Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric
Author(s) Li, Gang
Tong, F.
Dai, Honghua
Conference name IEEE International Conference on Data Mining (2001 : San Jose, Calif.)
Conference location San Jose, California
Conference dates 29 November - 2 December 2001
Title of proceedings Proceedings : 2001 IEEE International Conference on Data Mining, 29 November--2 December 2001, San Jose, California
Editor(s) Cercone, Nick
Lin, T.Y.
Wu, Xindong
Publication date 2001
Start page 615
End page 616
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Summary This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.
ISBN 0769511198
9780769511191
Language eng
Field of Research 080403 Data Structures
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ┬ęThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004630

Document type: Conference Paper
Collections: School of Information Technology
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