Advertisement

Modular Rough Neuro-fuzzy Systems for Classification

  • Rafał Scherer
  • Marcin Korytkowski
  • Robert Nowicki
  • Leszek Rutkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4967)

Abstract

In the paper we propose a new class of modular systems for classification in the case of missing features. We incorporate the rough set theory into construction of neuro-fuzzy systems which create the modular structure. The AdaBoost algorithm is combined with the gradient algorithm to train the whole system. We illustrate the performance of our approach on typical benchmarks.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, Irvine, University of California, Department of Information and Computer Science (1998), www.ics.uci.edu/~mlearn/MLRepository.html
  2. 2.
    Breiman, L.: Bias, variance, and arcing classifiers, Technical Report 460, Statistics Department, University of California (1997)Google Scholar
  3. 3.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Internat. J. General Systems 17(2-3), 191–209 (1990)CrossRefGoogle Scholar
  4. 4.
    Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advences of the Rough Sets Theory, pp. 203–232. Kluwer, Dordrecht (1992)Google Scholar
  5. 5.
    Korytkowski, M., Rutkowski, L., Scherer, R.: On Combining Backpropagation with Boosting. In: 2006 International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada (2006)Google Scholar
  6. 6.
    Meir, R., Ratsch, G.: An Introduction to Boosting and Leveraging. In: Mendelson, S., Smola, A.J. (eds.) Advanced Lectures on Machine Learning. LNCS (LNAI), vol. 2600, pp. 118–183. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Nowicki, R.: Rough Sets in the Neuro-Fuzzy Architectures Based on Monotonic Fuzzy Implications. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 510–517. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Nowicki, R.: Rough-Neuro-Fuzzy System with MICOG Defuzzification. In: Proc. 2006 IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, pp. 9090–9097 (2006)Google Scholar
  9. 9.
    Pawlak, Z.: Rough sets. International Journal of Information and Computer Science 11(341), 341–356 (1982)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht (1991)zbMATHGoogle Scholar
  11. 11.
    Pawlak, Z.: Rough sets, decision algorithms and Bayes’ theorem. European Journal of Operational Research 136, 181–189 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Polkowski, L.: Rough Sets. Mathematical Foundation. Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York (2002)Google Scholar
  13. 13.
    Rutkowski, L., Cpałka, K.: Flexible Neuro-Fuzzy Systems. IEEE Trans. Neural Networks 14(3), 554–574 (2003)CrossRefGoogle Scholar
  14. 14.
    Rutkowski, L., Cpałka, K.: Designing and Learning of Adjustable Quasi-Triangular Norms With Applications to Neuro-Fuzzy Systems. IEEE Trans. Fuzzy Systems 13(1), 140–151 (2005)CrossRefGoogle Scholar
  15. 15.
    Schapire, R.E.: A brief introduction to boosting. In: Proc. of the Sixteenth International Joint Conference on Artificial Intelligence 1999, pp. 1401–1406 (1999)Google Scholar
  16. 16.
    Wang, L.X.: Adaptive Fuzzy Systems and Control. PTR Prentice Hall, Englewood Cliffs (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rafał Scherer
    • 1
    • 2
  • Marcin Korytkowski
    • 1
    • 3
  • Robert Nowicki
    • 1
    • 2
  • Leszek Rutkowski
    • 1
    • 2
  1. 1.Department of Computer EngineeringCzȩstochowa University of TechnologyCzȩstochowaPoland
  2. 2.Department of Artificial IntelligenceAcademy of Humanities and Economics in LodzŁódźPoland
  3. 3.Olsztyn Academy of Computer Science and ManagementOlsztynPoland

Personalised recommendations