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
收藏
页码：174 / 178
页数：5
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