• DocumentCode
    3337033
  • Title

    Feature selection method based on the improved of mutual information and genetic algorithm

  • Author

    Qiu Ye ; Liu Peiyu ; Yang Yuzhen

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Shandong Normal Univ., Ji´nan, China
  • Volume
    1
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    836
  • Lastpage
    839
  • Abstract
    The feature selection is a key method of text categorization technology, this paper proposed a text feature selection method based on the improved of mutual information and genetic algorithm. Used the improved of mutual information algorithm to do the initial choose to removing redundancy and noise words at first, and then used the genetic algorithm to training the template which generate by a subset of words, so get the optimal feature subset that on behalf of the issue space, to achieve dimensionality reduction and improved classification accuracy.
  • Keywords
    feature extraction; genetic algorithms; set theory; text analysis; feature selection method; genetic algorithm; mutual information algorithm; optimal feature subset; text categorization technology; Computational complexity; Evolution (biology); Frequency; Genetic algorithms; Genetic engineering; Information science; Mutual information; Noise generators; Noise reduction; Text categorization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    IT in Medicine & Education, 2009. ITIME '09. IEEE International Symposium on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-3928-7
  • Electronic_ISBN
    978-1-4244-3930-0
  • Type

    conf

  • DOI
    10.1109/ITIME.2009.5236305
  • Filename
    5236305