Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach

  • Marian B. Gorzałczany
  • Filip Rudziński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)


The paper presents a generalization of the Pittsburgh approach to learn fuzzy classification rules from data. The proposed approach allows us to obtain a fuzzy rule-based system with a predefined level of compromise between its accuracy and interpretability (transparency). The application of the proposed technique to design the fuzzy rule-based classifier for the well known benchmark data sets (Dermatology and Wine) available from the is presented. A comparative analysis with several alternative (fuzzy) rule-based classification techniques has also been carried out.


Membership Function Fuzzy Rule Rule Base Input Attribute Possibility Distribution 
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.
    Alcala, R., Nojima, Y., Herrera, F., Ishibuchi, H.: Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1–16 (2010)Google Scholar
  2. 2.
    Baczyński, M., Jayaram, B.: Fuzzy Implications. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  3. 3.
    Chang, X., Lilly, J.H.: Evolutionary Design of a Fuzzy Classifier from Data. IEEE Transactions on Systems, Man and Cybernetics, Part B 34(4), 1894–1906 (2004)CrossRefGoogle Scholar
  4. 4.
    Chen, J., Hou, Y., Xing, Z., Jia, L., Tong, Z.: A Multi-objective Genetic-based Method for Design Fuzzy Classification Systems. IJCSNS International Journal of Computer Science and Network Security 6(8A), 110–117 (2006)Google Scholar
  5. 5.
    Cios, K.J. (ed.): Medical Data Mining and Knowledge Discovery. Physica-Verlag, Springer-Verlag Co., Heidelberg, New York (2001)zbMATHGoogle Scholar
  6. 6.
    Cordon, O.: A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of Approximate Reasoning 52, 894–913 (2011)CrossRefGoogle Scholar
  7. 7.
    Gacto, M.J., Alcala, R., Herrera, F.: Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures. Information Sciences 181(20), 4340–4360 (2011)CrossRefGoogle Scholar
  8. 8.
    Gorzałczany M.B.: Computational Intelligence Systems and Applications, Neuro-Fuzzy and Fuzzy Neural Synergisms. Physica-Verlag, Springer-Verlag Co., Heidelberg, New York (2002)Google Scholar
  9. 9.
    Gorzałczany, M.B., Rudziński, F.: A Modified Pittsburg Approach to Design a Genetic Fuzzy Rule-Based Classifier from Data. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6113, pp. 88–96. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Ishibuchi, H., Nakashima, T., Murata, T.: Three-objective genetics-based machine learning for linquistic extraction. Information Sciences 136(1-4), 109–133 (2001)zbMATHCrossRefGoogle Scholar
  11. 11.
    Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook. Springer, New York (2005)zbMATHGoogle Scholar
  12. 12.
    Ponce J., Karahoca A. (eds): Data Mining and Knowledge Discovery in Real Life Applications. IN-TECH, Vienna (2009)Google Scholar
  13. 13.
    Rutkowski, L.: Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation. Kluwer Academic Publishers, Boston (2004)zbMATHGoogle Scholar
  14. 14.
    Stavros, L., Ludmil, M.: Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artificial Intelligence in Medicine 50, 117–126 (2010)CrossRefGoogle Scholar
  15. 15.
    Ubeyli, E.D., Guler, I.: Automatic detection of erythemato squmous diseases using adaptive neuro-fuzzy inference systems. Computer in Biology and Medicine 35, 421–433 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marian B. Gorzałczany
    • 1
  • Filip Rudziński
    • 1
  1. 1.Department of Electrical and Computer EngineeringKielce University of TechnologyKielcePoland

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