Using Rough Sets Theory and Minimum Description Length Principle to Improve a β-TSK Fuzzy Revision Method for CBR Systems

  • Florentino Fdez-Riverola
  • Fernando Díaz
  • Juan M. Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3171)


This paper examines a fuzzy logic based method that automates the review stage of a 4-step Case Based Reasoning system and aids in the process of obtaining an accurate solution. The proposed method has been derived as an extension of the Sugeno Fuzzy model, and evaluates different solutions by reviewing their score in an unsupervised mode. In addition, this paper proposes an improvement of the original fuzzy revision method based on the reduction of the original set of attributes that define a case. This task is performed by a feature subset selection algorithm based on the Rough Set theory and the minimum description length principle.


CBR TSK fuzzy models rough sets minimum description length automated revision stage 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Watson, I.: Applying Case-based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Mateo (1997)zbMATHGoogle Scholar
  2. 2.
    Pal, S.K., Dilon, T.S., Yeung, D.S.: Soft Computing in Case Based Reasoning. Springer, London (2000)Google Scholar
  3. 3.
    Lenz, M., Bartsch-Sprl, B., Burkhard, H.-D., Wess, S. (eds.): Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400. Springer, Heidelberg (1998)Google Scholar
  4. 4.
    Bergmann, R., Breen, S., Göker, M., Manago, M., Wess, S. (eds.): Developing Industrial Case-Based Reasoning Applications. LNCS, vol. 1612. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  5. 5.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR Systems using Kernel Methods. International Journal of Intelligent Systems 16(4), 574–586 (2001)CrossRefGoogle Scholar
  6. 6.
    Corchado, J.M., Aiken, J., Corchado, E.S., Fyfe, C., Fdez-Riverola, F., González, M.: Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems. In: Proc. of the Fifth International Conference on Case-Based Reasoning, pp. 107–121 (2003)Google Scholar
  7. 7.
    Corchado, J.M., Aiken, J.: Hybrid Artificial Intelligence Methods in Oceanographic Forecasting Models. IEEE SMC Transac. Part C (2003)Google Scholar
  8. 8.
    Smyth, B., McKenna, E.: Modelling the Competence of Case-Bases. In: Proc. of Fourth European Workshop on Case-Based Reasoning, pp. 221–232 (1998)Google Scholar
  9. 9.
    De, R.K., Pal, S.K.: Case Based Systems: A Neuro-Fuzzy Method for Selecting Cases. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft computing in Case Based Reasoning, pp. 241–257. Springer, Heidelberg (2000)Google Scholar
  10. 10.
    Shiu, S.C.K., Wang, X.Z., Yeung, D.S.: Neuro-Fuzzy Approach for Maintanining Case Bases. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft computing in Case Based Reasoning, pp. 259–273. Springer, Heidelberg (2000)Google Scholar
  11. 11.
    Leake, D.B., Wilson, D.C.: Categorizing Case-Base Maintenance: Dimensions and Directions. In: Proc. of the Fourth European Workshop on Case-Based Reasoning, pp. 196–207 (1998)Google Scholar
  12. 12.
    Main, J., Dillon, T.S.: A Neuro-Fuzzy Methodology for Case Retrieval and an Object- Oriented Case Schema for Structuring Case Bases and their Application to Fashion Footwear Design. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft computing in Case Based Reasoning, pp. 276–291. Springer, London (2000)Google Scholar
  13. 13.
    Jeng, B.C., Liang, T.P.: Fuzzy Indexing and Retrieval in Case-Based Systems. Expert Systems with Applications 8(1), 135–142 (1995)CrossRefGoogle Scholar
  14. 14.
    Cheetham, B., Cuddihy, P., Goebel, K.: Applications of Soft CBR al General Electric. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft computing in Case Based Reasoning, pp. 335–365. Springer, London (2000)Google Scholar
  15. 15.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transac. on Systems, Man and Cybernetics 15, 116–132 (1985)zbMATHGoogle Scholar
  16. 16.
    Towell, G., Shavlik, J.: Extracting refined fuzzy rules from knowledge-based neural networks. Machine Learning 13, 71–101 (1993)Google Scholar
  17. 17.
    Jin, Y., von Seelen, W., Sendhoff, B.: On generating FC3 fuzzy rule systems from data using evolution strategies. IEEE Transac. on Systems, Man and Cybernetics 29(6), 829–845 (1999)CrossRefGoogle Scholar
  18. 18.
    Fritzke, B.: Fast learning with incremental RBF Networks. Neural Processing Letters 1(1), 2–5 (1994)CrossRefGoogle Scholar
  19. 19.
    Jin, Y., von Seelen, W., Sendhoff, B.: Extracting Interpretable Fuzzy Rules from RBF Neural Networks, Technical Institut für Neuroinformatik, Ruhr-Universität Bochum (January 2000) Google Scholar
  20. 20.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowledge-Based Systems 16(5-6), 321–328 (2003)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Fdez-Riverola, F., Corchado, J.M.: An automated CBR Revision method based on a set of β-TSK Fuzzy models. In: Proc. of the X CAEPIA - V TTIA, vol. 1, pp. 395–404 (2003)Google Scholar
  22. 22.
    Setnes, M., Babuška, R., Kaymak, U., Lemke, R.: Similarity measures in fuzzy rule base simplification. IEEE Transac. on Systems, Man and Cybernetics 28, 376–386 (1998)CrossRefGoogle Scholar
  23. 23.
    Fdez-Riverola, F.: Neuro-symbolic model for unsupervised forecasting of changing environments. Ph.D. diss., Dept. of Computer Science, Vigo University, Spain (2002)Google Scholar
  24. 24.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  26. 26.
    Pawlak, Z.: Rough sets: present state and the future. Foundations of Computing and Decision Sciences 11(3-4), 157–166 (1993)MathSciNetGoogle Scholar
  27. 27.
    Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39–59 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Düntsch, I., Gediga, G.: Statistical evaluation of rough set dependency analysis. International Journal of Human-Computer Studies 46, 589–604 (1997)CrossRefGoogle Scholar
  29. 29.
    Düntsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106, 77–107 (1998)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Díaz, F., Corchado, J.M.: A method based on the Rough Set theory and the MDL principle to select relevant features. In: Proc. of the X CAEPIA - V TTIA, vol. 1, pp. 101–104 (2003)Google Scholar
  31. 31.
    Rissanen, J.: Minimum description length principle. In: Kotz, S., Johnson, N.L. (eds.) Encyclopedia of Statistical Sciences, pp. 523–527. John Wiley and Sons, New York (1985)Google Scholar
  32. 32.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Florentino Fdez-Riverola
    • 1
  • Fernando Díaz
    • 1
  • Juan M. Corchado
    • 2
  1. 1.Dept. InformáticaUniversity of Vigo, Escuela Superior de Ingeniería Informática, Edificio PolitécnicoOurenseSpain
  2. 2.Dept. de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

Personalised recommendations