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A Modified Neural Network Classifier with Adaptive Weight Update and GA-Based Feature Subset Selection

  • Jinhai Liu
  • Zhibo Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

This paper proposes a new neural network classifier system with adaptive weight update. The system is divided into two sections namely, feature subset selection section and classification section. Genetic algorithm is introduced to complete feature subset selection to save the cost of training dataset. Classification section is inspired by a further research on the weight coefficient of membership function in “Data-Core-Based Fuzzy Min-Max Neural Network”(DCFMN).The modified classifier can improve the classification accuracy when training data is much smaller than testing data where this situation often occurs in real word due to its capacity of updating its weight coefficient while testing data online. This ability is really indispensible to classify unlabeled dataset such as field data for fault detection. The proposed modified classifier is tested on data-base available online. Results demonstrate the good qualities of this new neural network classifier.

Keywords

DCFMN genetic algorithm feature subset selection Fuzzy min-max neural network Classifier Weight value update 

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References

  1. 1.
    Simpson, P.K.: Fuzzy min-max neural networks-part I: Classification. IEEE Trans. Neural Networks 3, 776–786 (1992)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition, Piscataway, NewYork (1992)Google Scholar
  3. 3.
    Sushmita, M., Sankar, K.P.: Fuzzy sets in pattern recognition and machine intelligence. Fuzzy Sets Syst. 156(3), 381–386 (2005)CrossRefGoogle Scholar
  4. 4.
    Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst. 52(1), 21–32 (1992)CrossRefGoogle Scholar
  5. 5.
    Abe, S., Lan, M.S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Syst. 3(1), 18–28 (1995)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Jahromi, M.Z., Taheri, M.: A proposed method for learning ruleweights in fuzzy rule-based classification systems. Fuzzy Sets Syst. 159(4), 449–459 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Netwworks 11(3), 769–783 (2000)CrossRefGoogle Scholar
  8. 8.
    Nandedkar, A.V., Biswas, P.K.: A fuzzy min-max neural network classifier with compensatory neuron architecture. IEEE Trans. Neural Networks 18(1), 42–54 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, H., Liu, J., Ma, D., Wang, Z.: Data-core-based fuzzy min-max neural network for pattern classification. IEEE Trans. Neural Networks 22(12), 2339–2352 (2011)CrossRefGoogle Scholar
  10. 10.
    Harrag, A., Saigaa, D., Boukharouba, K., Drif, M., Bouchelaghem, A.: GA-based Feature Subset Selection Application to Arabic Speaker Recognition System. In: 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 382–387. IEEE Press, New York (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jinhai Liu
    • 1
  • Zhibo Yu
    • 1
  1. 1.School of Information Science and EngineeringNortheastern UniversityShenyangP.R. China

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