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

Man Leung WONG; Wai LAM; Kwong Sak LEUNG

doi:10.1109/34.748825

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

Man Leung WONG, Wai LAM, Kwong Sak LEUNG

Lingnan College

Research output: Journal Publications › Journal Article (refereed) › peer-review

88 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	174-178
Number of pages	5
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	21
Issue number	2
DOIs	https://doi.org/10.1109/34.748825
Publication status	Published - 1 Jan 1999

Keywords

Bayesian networks
Evolutionary computation
Genetic algorithms
Minimum description length principle
Unsupervised learning

Access to Document

10.1109/34.748825

Cite this

@article{e851eb3e9985401e91fdcf3f2e765a08,

title = "Using evolutionary programming and minimum description length principle for data mining of Bayesian networks",

abstract = "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.",

keywords = "Bayesian networks, Evolutionary computation, Genetic algorithms, Minimum description length principle, Unsupervised learning",

author = "WONG, {Man Leung} and Wai LAM and LEUNG, {Kwong Sak}",

year = "1999",

month = jan,

day = "1",

doi = "10.1109/34.748825",

language = "English",

volume = "21",

pages = "174--178",

journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",

issn = "0162-8828",

publisher = "IEEE Computer Society",

number = "2",

}

TY - JOUR

T1 - Using evolutionary programming and minimum description length principle for data mining of Bayesian networks

AU - WONG, Man Leung

AU - LAM, Wai

AU - LEUNG, Kwong Sak

PY - 1999/1/1

Y1 - 1999/1/1

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - Evolutionary computation

KW - Genetic algorithms

KW - Minimum description length principle

KW - Unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=0033076357&partnerID=8YFLogxK

U2 - 10.1109/34.748825

DO - 10.1109/34.748825

M3 - Journal Article (refereed)

SN - 0162-8828

VL - 21

SP - 174

EP - 178

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

IS - 2

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this