Using evolutionary programming and minimum description length principle for data mining of Bayesian networks

被引：0

作者：

Wong, ML ^{[1
]}

Lam, W

Leung, KS

机构：

[1] Lingnan Coll, Dept Informat Syst, Tuen Mun, Peoples R China

[2] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Peoples R China

[3] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Peoples R China

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1999年 / 21卷 / 02期

关键词：

evolutionary computation; Bayesian networks; unsupervised learning; minimum description length principle; genetic algorithms;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We have developed a new approach (MDLEP) to learning Bayesian network structures based on the Minimum Description Length (MDL) principle and Evolutionary Programming (EP). 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.

引用

页码：174 / 178

页数：5