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

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conference contribution
posted on 2001-01-01, 00:00 authored by Gang LiGang Li, F Tong, Honghua Dai
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.

History

Title of proceedings

Proceedings : 2001 IEEE International Conference on Data Mining, 29 November--2 December 2001, San Jose, California

Event

IEEE International Conference on Data Mining (2001 : San Jose, Calif.)

Pagination

615 - 616

Publisher

IEEE Computer Society

Location

San Jose, California

Place of publication

Los Alamitos, Calif.

Start date

2001-11-29

End date

2001-12-02

ISBN-13

9780769511191

ISBN-10

0769511198

Language

eng

Publication classification

E1 Full written paper - refereed; E Conference publication

Copyright notice

This 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.

Editor/Contributor(s)

N Cercone, T Lin, X Wu

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