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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.

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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
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  2. Filip Rudziński
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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

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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

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  • 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)

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