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Methodology for Optimizing Fuzzy Classifiers Based on Computational Intelligence

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Book cover Computational Intelligence. Theory and Applications (Fuzzy Days 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2206))

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Abstract

In this paper a methodology using evolutionary algorithms is introduced for the optimization of fuzzy classifiers based on B-splines. The proposed algorithm maximizes the performance and minimizes the size of the classifier. On a well-known classification problem the algorithm performs an input selection over 9 observed characteristics yielding in a statement which attributes are important with respect to diagnose malignant or benign type of cancer.

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References

  1. Mangasarin, O.L, Wolberg, W.H.: Cancer Diagnosis via linear programming. SIAM News 23(5), (1990) 1–18.

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  4. Renners I., Saavedra E., Grauel A.: Data Mining and Classification Techniques based on Methods of Computational Intelligence. Springer-Verlag, Berlin-Heidelberg, Germany, (2001).

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  5. Cox, M.G.: Algorithms for Spline Curves and Surfaces. NPL Report DITC 166/90, (1990) 36 pages.

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Author information

Authors and Affiliations

  1. Paderborn University, Soest Campus, Soest, 59494, Germany

    Ingo Renners, Adolf Grauel & Ernesto Saavedra

Authors
  1. Ingo Renners
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  2. Adolf Grauel
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  3. Ernesto Saavedra
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Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, 44221, Dortmund, Germany

    Bernd Reusch

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© 2001 Springer-Verlag Berlin Heidelberg

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Renners, I., Grauel, A., Saavedra, E. (2001). Methodology for Optimizing Fuzzy Classifiers Based on Computational Intelligence. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_42

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  • DOI: https://doi.org/10.1007/3-540-45493-4_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42732-2

  • Online ISBN: 978-3-540-45493-9

  • eBook Packages: Springer Book Archive

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