Prototype-Based Threshold Rules

  • Marcin Blachnik
  • Włodzisław Duch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


Understanding data is usually done extracting fuzzy or crisp logical rules using neurofuzzy systems, decision trees and other approaches. Prototype-based rules are an interesting alternative providing in many cases simpler, more accurate and more comprehensible description of the data. Algorithm for generation of threshold prototype-based rules are described and a comparison with neurofuzzy systems on a number of datasets provided. Results show that systems for data understanding generating prototypes deserve at least the same attention as that enjoyed by the neurofuzzy systems.


Fuzzy Rule Rule Extraction Training Vector Machine Learning Database Threshold Rule 
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.


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  1. 1.
    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
  2. 2.
    Ye, N.: The handbook of data mining. Lawrence Erlbaum Associates, London (2003)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  4. 4.
    Breiman, L., Friedman, J.H., Oslhen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)MATHGoogle Scholar
  5. 5.
    Grąbczewski, K., Duch, W.: The separability of split value criterion. In: 5th Conference on Neural Network and Soft Computing, pp. 201–208. Polish Neural Network Society, Zakopane, Poland (2000)Google Scholar
  6. 6.
    Jankowski, N., Grąbczewski, K., Duch, W., Naud, A., Adamczak, R.: Ghostminer data mining software,
  7. 7.
    Pedrycz, W.: Fuzzy set technology in knowledge discovery. Fuzzy Sets and Systems 98, 279–290 (1998)CrossRefGoogle Scholar
  8. 8.
    Duch, W.: Similarity based methods: a general framework for classification, approximation and association. Control and Cybernetics 29(4), 937–968 (2000)MATHMathSciNetGoogle Scholar
  9. 9.
    Duch, W., Grudziński, K.: Prototype based rules - a new way to understand the data. In: Proc. of the International Joint Conference on Neural Networks (IJCNN) 2001, Washington D.C, USA, pp. 1858–1863 (2001)Google Scholar
  10. 10.
    Grąbczewski, K., Duch, W.: Heterogenous forests of decision trees. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 504–509. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)MATHGoogle Scholar
  12. 12.
    Nauck, D., Klawonn, F., Kruse, R.: Foundations on Neuro-Fuzzy Systems. J. Wiley, New York (1997)Google Scholar
  13. 13.
    Pal, S.K., Mitra, S.: Neuro-Fuzzy Pattern Recognition. J. Wiley, New York (1999)Google Scholar
  14. 14.
    Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)MATHMathSciNetGoogle Scholar
  15. 15.
    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
  16. 16.
    Mertz, C.J., Murphy, P.M.: UCI repository of machine learning databases,
  17. 17.
  18. 18.
    Walker, A.J., Cross, S.S., Harrison, R.F.: Visualization of biomedical datasets by use of growing cell structure networks: a novel diagnostic classification technique. Lancet 354, 1518–1522 (1999)CrossRefGoogle Scholar
  19. 19.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  20. 20.
    Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood (1994)Google Scholar
  21. 21.
    Shakhnarovish, G., Darrell, T., Indyk, P. (eds.): Nearest-Neighbor Methods in Learning and Vision. MIT Press, Cambridge (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcin Blachnik
    • 1
  • Włodzisław Duch
    • 2
    • 3
  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

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