Fuzzy set methods in pattern recognition

  • James M. Keller
  • Hongjie Qiu
Fuzzy Set And Pattern Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


Dealing with uncertainty is a common problem in pattern recognition. Rarely do object descriptions from different classes fall into totally disjoint regions of feature space. This uncertainty in class definition can be handled in several ways. In this paper we present several approaches to the incorporation of fuzzy set information into pattern recognition. We then introduce a new technique based on the fuzzy integral which combines objective evidence with the importance of that feature set for recognition purposes. In effect, the fuzzy integral performs a local feature selection, in that it attempts to use the strongest measurements first in the object classification. Algorithm performance is illustrated on real and synthetic data sets.


Membership Function Class Membership Fuzzy Measure Sample Vector Feature Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • James M. Keller
    • 1
  • Hongjie Qiu
    • 1
  1. 1.Electrical and Computer EngineeringUniversity of Missouri-ColumbiaColumbia

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