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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2010: Artificial Intelligence and Soft Computing pp 88–96Cite as

A Modified Pittsburg Approach to Design a Genetic Fuzzy Rule-Based Classifier from Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Abstract

The paper presents a modification of the Pittsburg approach to design a fuzzy classifier from data. Original, non-binary crossover and mutation operators are introduced. No special coding of fuzzy rules and their parameters is required. The application of the proposed technique to design the fuzzy classifier for the well known benchmark data set (Wisconsin Breast Cancer) 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. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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© 2010 Springer-Verlag Berlin Heidelberg

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Gorzałczany, M.B., Rudziński, F. (2010). 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) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-13208-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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