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Fuzzy Rule-Based Systems Derived from Similarity to Prototypes

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Neural Information Processing (ICONIP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3316))

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Abstract

Relations between similarity-based systems, evaluating similarity to some prototypes, and fuzzy rule-based systems, aggregating values of membership functions, are investigated. Fuzzy membership functions lead to new types of similarity measures and similarity measures, including probabilistic distance functions that are applicable to symbolic data, lead to new types of membership functions. Optimization of prototype-based rules is an interesting alternative to neurofuzzy systems. As an illustration simple prototype-based rules are found for leukemia gene expression data.

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Author information

Authors and Affiliations

  1. Department of Informatics, Nicholaus Copernicus University, Grudziadzka 5, Torun, Poland

    Włodzisław Duch

  2. School of Computer Engineering, Nanyang Technological University, Singapore

    Włodzisław Duch

  3. Division of Computer Methods, Department of Electrotechnology, The Silesian University of Technology, ul. Krasińskiego 8, 40-019, Katowice, Poland

    Marcin Blachnik

Authors
  1. Włodzisław Duch
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  2. Marcin Blachnik
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India

    Nikhil Ranjan Pal

  2. School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand

    Nik Kasabov

  3. Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India

    Rajani K. Mudi

  4. Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta,  

    Srimanta Pal

  5. Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India

    Swapan Kumar Parui

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© 2004 Springer-Verlag Berlin Heidelberg

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Duch, W., Blachnik, M. (2004). Fuzzy Rule-Based Systems Derived from Similarity to Prototypes. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_140

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  • DOI: https://doi.org/10.1007/978-3-540-30499-9_140

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive

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