• DocumentCode
    2264648
  • Title

    Fuzzy logic extension of nonparametric approach to feature selection and binary decision tree design

  • Author

    Mirshab, B. ; Anneberg, L.

  • Author_Institution
    Dept. of Electr. Eng., Lawrence Technol. Univ., Southfield, MI, USA
  • fYear
    1993
  • fDate
    16-18 Aug 1993
  • Firstpage
    828
  • Abstract
    The binary decision tree for pattern recognition systems based on the selection of a set of features is both capable of high discrimination and is economical. A further extension is proposed which will incorporate fuzzy logic decisions at each node as part of the feature selection process. Each node will return a possibilistic response: “possibly” or “possibly not” as the decision regarding a set p.f. patterns. The advantage to fuzzy decision nodes is that the input to the decision tree may be linguistic in nature and the tree could return a `crisp´ or defuzzified output, which will be a useful result
  • Keywords
    character recognition; feature extraction; fuzzy logic; image recognition; binary decision tree design; feature selection; fuzzy logic decisions; nonparametric approach; pattern recognition systems; Algorithm design and analysis; Clustering algorithms; Data analysis; Decision trees; Design engineering; Fuzzy logic; Fuzzy systems; Image processing; Pattern recognition; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
  • Conference_Location
    Detroit, MI
  • Print_ISBN
    0-7803-1760-2
  • Type

    conf

  • DOI
    10.1109/MWSCAS.1993.343197
  • Filename
    343197