• DocumentCode
    1946248
  • Title

    New algorithm for constructing Bayesian network structures from data

  • Author

    Gao, Xiao-Li ; Li, Bing-Han ; Liu, San-Yang

  • Author_Institution
    Dept. of Sci., Xi´´Dian Univ., Xi´´an, China
  • fYear
    2010
  • fDate
    15-16 Nov. 2010
  • Firstpage
    360
  • Lastpage
    364
  • Abstract
    Bayesian network is an uncertainty inference network based on probability. Its structure learning is one of the main research techniques in the field of data mining and knowledge discovering, while constructing Bayesian network structures from data is NP hard. According to the information theory and conditional independence test, a new algorithm is presented for the construction of optimal Bayesian network structure, and numerical experiments show that the structure with highest degree of data matching can be much faster determined by the new algorithm, thus the study of Bayesian network structures becomes more efficient.
  • Keywords
    belief networks; computational complexity; data mining; directed graphs; inference mechanisms; information theory; optimisation; probability; uncertainty handling; Bayesian network; data matching; data mining; data structure; directed acyclic graph; information theory; knowledge discovery; optimal network; probability; uncertainty inference network; Algorithm design and analysis; Bayesian methods; Complexity theory; Equations; Learning; Mutual information; Random variables; Bayesian network; conditional independence test; connected graph; essential graph; mutual information;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4244-6791-4
  • Type

    conf

  • DOI
    10.1109/ISKE.2010.5680848
  • Filename
    5680848