• DocumentCode
    2067662
  • Title

    Classification rule mining research based on hybrid genetic algorithm

  • Author

    Wang Xiangrui ; Wang Shuai

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Jilin Inst. of Archit. & Civil Eng., Changchun, China
  • fYear
    2011
  • fDate
    16-18 Dec. 2011
  • Firstpage
    292
  • Lastpage
    294
  • Abstract
    Traditional classification rule mining based on genetic algorithm usually exists the problem of low quality of the mined rules, too many redundant rules in the population after optimization, and inaccuracy in classification. The paper analyzes the principles of classification rules mining, and proposes that classification rules mining methods based on hybrid genetic algorithm can effectively overcome the above disadvantages and improve the accuracy of classification rule mining.
  • Keywords
    data mining; genetic algorithms; pattern classification; classification rule mining research; hybrid genetic algorithm; mined rules; optimization; redundant rules; traditional classification rule mining; Accuracy; Biological cells; Classification algorithms; Data mining; Genetic algorithms; Genetics; Training; Classification Rules; Data Mining; Genetic Algorithm; Hybrid Genetic Algorithms; Local Search;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-4577-1700-0
  • Type

    conf

  • DOI
    10.1109/TMEE.2011.6199200
  • Filename
    6199200