A Neuro-Fuzzy Scheme for Integrated Input Fuzzy Set Selection and Optimal Fuzzy Rule Generation for Classification

  • Santanu Sen
  • Tandra Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

This paper proposes a scheme for designing a classier along with fuzzy set selection. It detects discontinuities in the domain of input and prunes the corresponding fuzzy sets using a neuro-fuzzy architecture. This reduces the size of the network and so the number of rules. The network is trained in three phases. In the rst phase, the network learns the important fuzzy sets. In the subsequent phases, the network is pruned to produce optimal rule set by pruning conicting and less significant rules. We use a four-layered feed-forward network and error back propagation learning. The second layer of the network learns a modulator function for each input fuzzy set that identies the unnecessary fuzzy sets. In this paper, we also introduced the notion of utility factors for fuzzy rules. Rules with small utility factors are less signicant or less used rules and can be eliminated. They are learned and detected by the third layer. After training in phase 3 in its reduced structure, the system retains almost the same level of performance. The proposed system has been tested on synthetic data set and found to perform well.

References

  1. 1.
    Pal, N.R., Pal, T.: On rule pruning using fuzzy neural networks. Fuzzy Sets Syst. 106, 335–347 (1999)MATHCrossRefGoogle Scholar
  2. 2.
    Shann, J.J., Fu, H.C.: A fuzzy neural network for rule acquiring on fuzzy control systems. Fuzzy Sets Syst. 71, 345–357 (1995)CrossRefGoogle Scholar
  3. 3.
    Chakraborty, D., Pal, N.R.: Integrated Feature Analysis and Fuzzy Rule-Based System Identification in a Neuro-Fuzzy Paradigm. IEEE Trans. Systems Man Cybernetics 31, 391–400 (2001)CrossRefGoogle Scholar
  4. 4.
    Chakraborty, D., Pal, N.R.: A Neuro-Fuzzy Scheme for Simultaneous Feature Selection and Fuzzy Rule-Based Classification. IEEE Trans. on Neural Networks 15(1), 110–123 (2004)CrossRefGoogle Scholar
  5. 5.
    Chakraborty, D., Pal, N.R.: Making a Multilayer Perceptron Network Say- “Don’t Know” When It Should. In: ICONIP, Orchid Country Club, Singapore (November 18-22, 2002)Google Scholar
  6. 6.
    Ishibuchi, H., Nakashima, T., Morisawa, T.: Voting in fuzzy rule-based systems for pattern classification problem. Fuzzy Sets Syst. 103, 223–238 (1999)CrossRefGoogle Scholar
  7. 7.
    Krishnapuram, R., Lee, J.: Propagation of uncertainty in neural networks. In: Proc. SPIE Conf. Robot, Computer Vision, Bellingham, WA, pp.377—383 (1988)Google Scholar
  8. 8.
    Nozaki, K., Isibuchi, H., Tanaka, H.: Adaptive fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 4, 238–250 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Santanu Sen
    • 1
  • Tandra Pal
    • 2
  1. 1.Tejas Networks India Ltd., Bangalore - 560078India
  2. 2.Department of Computer Science and Engineering, National Institute of Technology, Durgapur 713209India

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