Advertisement

Classifier Systems Based on Possibility Distributions: A Comparative Study

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

Abstract

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.

Keywords

Classifier System Fuzzy Neural Network Possibility Distribution Possibility Approach Fuzzy Network 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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

Personalised recommendations