• DocumentCode
    2285090
  • Title

    Research on Feature Selection Algorithm Based on Mixed Model

  • Author

    He, Ming

  • Author_Institution
    Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    70
  • Lastpage
    72
  • Abstract
    Reduct finding, especially optimal reduct finding, similar to feature selection problem, is a crucial task in rough set applications to data mining. In this paper, we have studied the basic concepts of rough set theory, and discussed several special cases of the ant colony optimization metaheuristic algorithms. Based on the above study, we propose a feature selection algorithm within a mixed framework based on rough set theory and ant colony optimization. experimental results show that the algorithm of this paper is flexible for feature selection.
  • Keywords
    data mining; feature extraction; rough set theory; ant colony optimization metaheuristic algorithms; data mining; feature selection algorithm; mixed model; rough set theory; Ant colony optimization; Computer science; Data mining; Educational institutions; Filters; Helium; Information systems; Pattern recognition; Search methods; Set theory; ant colony optimization; feature selection; rough set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Electrical Engineering, 2008. ICCEE 2008. International Conference on
  • Conference_Location
    Phuket
  • Print_ISBN
    978-0-7695-3504-3
  • Type

    conf

  • DOI
    10.1109/ICCEE.2008.38
  • Filename
    4740948