Selection of Prototype Rules: Context Searching Via Clustering

  • Marcin Blachnik
  • Włodzisław Duch
  • Tadeusz Wieczorek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


Prototype-based rules are an interesting alternative to fuzzy and crisp logical rules, in many cases providing simpler, more accurate and more comprehensible description of the data. Such rules may be directly converted to fuzzy rules. A new algorithm for generation of prototype-based rules is introduced and a comparison with results obtained by neurofuzzy systems on a number of datasets provided.


Fuzzy Rule Learn Vector Quantization Balance Accuracy Prototype Selection Symbolic Feature 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2001)MATHGoogle Scholar
  2. 2.
    Duch, W.: Similarity based methods: a general framework for classification, approximation and association. Control and Cybernetics 29(4), 937–968 (2000)MATHMathSciNetGoogle Scholar
  3. 3.
    Duch, W., Adamczak, R., Diercksen, G.H.F.: Classification, Association and Pattern Completion using Neural Similarity Based Methods. Applied Mathematics and Computer Science 10(4), 101–120 (2000)Google Scholar
  4. 4.
    Duch, W., Setiono, R., Zurada, J.M.: Computational intelligence methods for understanding of data. Proc. of the IEEE 92(5), 771–805 (2004)CrossRefGoogle Scholar
  5. 5.
    Duch, W., Grudziński, K.: Prototype based rules - a new way to understand the data. In: Proc. of IJCNN 2001, Washington D.C., USA, pp. 1858–1863Google Scholar
  6. 6.
    Duch, W., Blachnik, M.: Fuzzy rule-based system derived from similarity to prototypes. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 912–917. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Nauck, D., Klawonn, F., Kruse, R.: Foundations on Neuro-Fuzzy Systems. Wiley, Chichester (1997)Google Scholar
  8. 8.
    Jankowski, N., Grochowski, M.: Comparison of Instance Selection Algorithms I. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Grochowski, M., Jankowski, N.: Comparison of Instance Selection Algorithms II. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 580–585. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Bezdek, J.C., Kuncheva, L.I.: Nearest prototype classifier designs: An experimental study. International Journal of Intelligent Systems 16(12), 1445–1473 (2001)MATHCrossRefGoogle Scholar
  11. 11.
    Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–268 (2000)MATHCrossRefGoogle Scholar
  12. 12.
    Pedrycz, W.: Conditional Fuzzy C-Means. Pattern Recognition Letters 17, 625–632 (1996)CrossRefGoogle Scholar
  13. 13.
    Maron, O., Moore, A.: The Racing Algorithm: Model Selection for Lazy Learners. Artificial Intelligence Review 11, 193–225 (1997)CrossRefGoogle Scholar
  14. 14.
    Mertz, C.J., Murphy, P.M.: UCI repository of machine learning databases,
  15. 15.
    Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communications of the ACM 29(12), 1213–1228 (1986)CrossRefGoogle Scholar
  16. 16.
    Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6 (1997)Google Scholar
  17. 17.
    Duch, W., Grudziński, K.: Meta-learning via search combined with parameter optimization. In: Intelligent Information Systems, Advances in Soft Computing, pp. 13–22. Physica Verlag (Springer), Heidelberg (2002)Google Scholar
  18. 18.
    Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines (and other kernel-based learning methods). Cambridge University Press, Cambridge (2000)Google Scholar
  19. 19.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, London (2006)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcin Blachnik
    • 1
  • Włodzisław Duch
    • 2
    • 3
  • Tadeusz Wieczorek
    • 1
  1. 1.Division of Computer Methods, Department of Electrotechnology and MetallurgyThe Silesian University of TechnologyKatowicePoland
  2. 2.Department of InformaticsNicolaus Copernicus UniversityToruńPoland
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingapore

Personalised recommendations