• DocumentCode
    2690581
  • Title

    Conditional independence based learning of bayesian classifiers guided by a variable ordering genetic search

  • Author

    Santos, Edimilson Batista dos ; Hruschka, Estevam R. ; Nicoletti, Maria Do Carmo

  • Author_Institution
    Fed. Univ. of Sao Carlos, Sao Carlos
  • fYear
    2007
  • fDate
    25-28 Sept. 2007
  • Firstpage
    1444
  • Lastpage
    1449
  • Abstract
    This work proposes a genetic strategy for learning a Bayesian classifier using an algorithm based on conditional independence and the information given by a variable ordering. The strategy has been implemented as the system VOGAC-PC. The paper presents and analyses the results of experiments in various domains using VOGAC-PC as well as a previous system, named VOGA-K2, based on algorithm K2.
  • Keywords
    Bayes methods; genetic algorithms; learning (artificial intelligence); pattern classification; search problems; Bayesian classifier; conditional independence; genetic strategy; learning algorithm; variable ordering genetic search; Bayesian methods; Biological cells; Convergence; Evolutionary computation; Flowcharts; Genetics; Iris;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-1339-3
  • Electronic_ISBN
    978-1-4244-1340-9
  • Type

    conf

  • DOI
    10.1109/CEC.2007.4424641
  • Filename
    4424641
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2690581