• DocumentCode
    1639442
  • Title

    An Improved TFIDF Feature Selection Algorithm Based On Information Entropy

  • Author

    Yantao, Zhou ; Jianbo, Tang ; Jiaqin, Wang

  • Author_Institution
    Hunan Univ., Changsha
  • fYear
    2007
  • Firstpage
    312
  • Lastpage
    315
  • Abstract
    The quality of text feature selection affects the accuracy of text categorization greatly. Due to the deficiency of traditional TFIDF without considering the distribution of feature words among classes, the paper analyzed the TFIDF feature selection algorithm, and proposed a new TFIDF feature selection method with concept of information entropy. Experimental results show the method is valid in improving the accuracy of text categorization.
  • Keywords
    data mining; text analysis; data mining; feature selection algorithm; information entropy; text categorization; text feature selection; Algorithm design and analysis; Data mining; Educational institutions; Frequency; Information analysis; Information entropy; Mutual information; Text categorization; TFIDF; data mining; feature selection; words information entropy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference, 2007. CCC 2007. Chinese
  • Conference_Location
    Hunan
  • Print_ISBN
    978-7-81124-055-9
  • Electronic_ISBN
    978-7-900719-22-5
  • Type

    conf

  • DOI
    10.1109/CHICC.2006.4346845
  • Filename
    4346845
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1639442