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

Authors
Wong, ML Lam, W Leung, KS
Citation
Ml. Wong et al., Using evolutionary programming and minimum description length principle for data mining of Bayesian networks, IEEE PATT A, 21(2), 1999, pp. 174-178
Citations number
17
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN journal
01628828 → ACNP
Volume
21
Issue
2
Year of publication
1999
Pages
174 - 178
Database
ISI
SICI code
0162-8828(199902)21:2<174:UEPAMD>2.0.ZU;2-7
Abstract
We have developed a new approach (MDLEP) to learning Bayesian network struc tures based on the Minimum Description Length (MDL) principle and Evolution ary Programming (EP). It employs a MDL metric, which is founded on informat ion theory, and integrates a knowledge-guided genetic operator for the opti mization in the search process.