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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

Pagination

615 - 616

Location

San Jose, California

Open access

  • Yes

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

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Editor/Contributor(s)

N Cercone, T Lin, X Wu

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