Skip to main content
SpringerLink
Log in

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

  • Conference paper
Swarm and Evolutionary Computation (EC 2012, SIDE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7269))

Abstract

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 http://archive.ics.uci.edu/ml is presented. A comparative analysis with several alternative (fuzzy) rule-based classification techniques has also been carried out.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. Baczyński, M., Jayaram, B.: Fuzzy Implications. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. Cios, K.J. (ed.): Medical Data Mining and Knowledge Discovery. Physica-Verlag, Springer-Verlag Co., Heidelberg, New York (2001)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  10. Ishibuchi, H., Nakashima, T., Murata, T.: Three-objective genetics-based machine learning for linquistic extraction. Information Sciences 136(1-4), 109–133 (2001)

    Article  MATH  Google Scholar 

  11. Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook. Springer, New York (2005)

    MATH  Google Scholar 

  12. Ponce J., Karahoca A. (eds): Data Mining and Knowledge Discovery in Real Life Applications. IN-TECH, Vienna (2009)

    Google Scholar 

  13. Rutkowski, L.: Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation. Kluwer Academic Publishers, Boston (2004)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Kielce University of Technology, Al. 1000-lecia P.P. 7, 25-314, Kielce, Poland

    Marian B. Gorzałczany & Filip Rudziński

Authors
  1. Marian B. Gorzałczany
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Filip Rudziński
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hallz, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gorzałczany, M.B., Rudziński, F. (2012). Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29353-5_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29352-8

  • Online ISBN: 978-3-642-29353-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation