• DocumentCode
    2416809
  • Title

    A Method for the Fuzzification of Categorical Variables

  • Author

    Jodoin, Etienne ; Reyes, Carlos Andrés Pena ; Sanchez, Eduardo

  • Author_Institution
    Swiss Fed. Inst. of Technol., Lausanne
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    831
  • Lastpage
    838
  • Abstract
    Besides the numeric variables which are common in fuzzy modeling, some variables involved in the description of specific behaviors are categorical. Such variables are discrete, have no order a-priori, and most of the time handle a large amount of values (e.g., genes, proteins, countries, religions, etc.). This paper proposes a methodology for the fuzzification of categorical variables which could make part of a larger fuzzy modeling approach. The proposed solution allows to automatically create fuzzy membership functions for nominal categorical variables. We study some parameters so as to better assess their possible effect on the final outcome of the whole fuzzy modeling process.
  • Keywords
    fuzzy logic; fuzzy systems; categorical variable fuzzification; fuzzy membership function; fuzzy modeling process; fuzzy system; Continents; Databases; Engines; Fuzzy systems; Inference algorithms; Logic; Multidimensional systems; Proteins; Support vector machines; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2006 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9488-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.2006.1681807
  • Filename
    1681807