Classifier Systems Based on Possibility Distributions: A Comparative Study

  • S. Singh
  • E. L. Hines
  • J. W. Gardner


The main aim of this paper is three fold: a) to understand the working of a classifier system based on possibility distribution functions, b) to evaluate its performance against other superior methods such as fuzzy and non-fuzzy neural networks on real data, c) and finally to recommend changes for enhancing its performance. The paper explains how to construct a possibility based classifier system which is used with conventional error-estimation techniques such as cross-validation and bootstrapping. The results were obtained on a set of electronic nose data and this performance was compared with earlier published results on the same data using fuzzy and non-fuzzy neural networks. The results show that the possibility approach is superior to the non-fuzzy approach, however, further work needs to be done.


Classifier System Fuzzy Neural Network Possibility Distribution Possibility Approach Fuzzy Network 
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  1. [1]
    B. Kosko. Neural Networks and Fuzzy Systems — A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, 1992.Google Scholar
  2. [2]
    E.H. Mamdani and B.R. Gaines, editors. Fuzzy Reasoning and its Applications. Academic Press, 1981.Google Scholar
  3. [3]
    S. Singh. Fuzzy neural networks for managing uncertainty. Master’s thesis, University of Warwick, UK, 1993.Google Scholar
  4. [4]
    S. Singh, E.L. Hines, and J.W. Gardner. Fuzzy neural computing of coffee and tainted water data on electronic noise. Sensors and Actuators B, 30(3):190–195, 1996.CrossRefGoogle Scholar
  5. [5]
    S. Singh and M. Steinl. Fuzzy search techniques in knowledge-based systems. In Proc. 5th Intl Conference on Data on Knowledge Systems for Manufacturing and Engineering. Reno, 1996.Google Scholar
  6. [6]
    P.D. Wasserman. Neural Computing: Theory and Practice. Van Nostrand Reinhold, NY, 1989.Google Scholar
  7. [7]
    S.M. Weiss and C.A. Kulikowski. Computer Systems that Learn. Morgan Kauffman, CA, 1991.Google Scholar
  8. [8]
    L.A. Zadeh. Fuzzy Logic and Its Applications. Academic Press, New York, 1965.Google Scholar
  9. [9]
    L.A. Zadeh. A Fuzzy-Algorithm Approach to the Definition of Complex or Imprecise Concepts, pages 147–192. John Wiley, 1987.Google Scholar
  10. [10]
    L.A. Zadeh. Fuzzy Sets as a Basis for a Theory of Possibility, pages 193–218. John Wiley, 1987.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • S. Singh
    • 1
  • E. L. Hines
    • 2
  • J. W. Gardner
    • 2
  1. 1.School of ComputingUniversity of PlymouthPlymouthUK
  2. 2.Department of EngineeringUniversity of WarwickCoventryUK

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