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

Man Leung WONG, Wai LAM, Kwong Sak LEUNG

    Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

    82 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 languageEnglish
    Pages (from-to)174-178
    Number of pages5
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume21
    Issue number2
    DOIs
    Publication statusPublished - 1 Jan 1999

    Fingerprint

    Evolutionary Programming
    Information theory
    Bayesian networks
    Bayesian Networks
    Evolutionary algorithms
    Data mining
    Mathematical operators
    Data Mining
    Genetic Operators
    Information Theory
    Network Structure
    Integrate
    Metric
    Optimization
    Knowledge
    Learning

    Keywords

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

    Cite this

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    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",
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    doi = "10.1109/34.748825",
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    pages = "174--178",
    journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
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    Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. / WONG, Man Leung; LAM, Wai; LEUNG, Kwong Sak.

    In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 2, 01.01.1999, p. 174-178.

    Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

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    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

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    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://commons.ln.edu.hk/sw_master/6911

    U2 - 10.1109/34.748825

    DO - 10.1109/34.748825

    M3 - Journal Article (refereed)

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    EP - 178

    JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

    JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

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