• DocumentCode
    3316745
  • Title

    Using Entropy to Impute Missing Data in a Classification Task

  • Author

    Delavallade, Thomas ; Dang, Thanh Ha

  • Author_Institution
    Univ. Pierre et Marie Curie - Paris6, Paris
  • fYear
    2007
  • fDate
    23-26 July 2007
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In real applications, part of the data is usually missing. But most techniques of data analysis and data mining can only deal with complete data. In this paper, a new taxonomy of imputation methods is proposed. Within this taxonomy a new technique, based on entropy measures is introduced. Its behaviour is studied through an empirical comparative analysis.
  • Keywords
    data analysis; data mining; entropy; pattern classification; data analysis; data classification; data mining; entropy measure; imputation method; Data analysis; Data mining; Databases; Decision trees; Entropy; Machine learning; Machine learning algorithms; Predictive models; Statistics; Taxonomy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International
  • Conference_Location
    London
  • ISSN
    1098-7584
  • Print_ISBN
    1-4244-1209-9
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2007.4295430
  • Filename
    4295430
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3316745