DocumentCode
1477770
Title
Using evolutionary programming and minimum description length principle for data mining of Bayesian networks
Author
Wong, Man Leung ; Lam, Wai ; Leung, Kwong Sak
Author_Institution
Dept. of Inf. Syst., Lingnan Coll., Tuen Mun, Hong Kong
Volume
21
Issue
2
fYear
1999
fDate
2/1/1999 12:00:00 AM
Firstpage
174
Lastpage
178
Abstract
We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process
Keywords
belief networks; data mining; genetic algorithms; information theory; search problems; unsupervised learning; Bayesian networks; data mining; evolutionary programming; genetic algorithm; information theory; knowledge-guided genetic operator; minimum description length; optimization; search process; unsupervised learning; Bayesian methods; Biological cells; Computer networks; Data mining; Entropy; Genetic algorithms; Genetic communication; Genetic programming; Information theory; Unsupervised learning;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.748825
Filename
748825
Link To Document