Skip to main content
SpringerLink
Log in

Fuzzy Rule-Based Systems Derived from Similarity to Prototypes

  • Conference paper
Neural Information Processing (ICONIP 2004)

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

Included in the following conference series:

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.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Nauck, D., Klawonn, F., Kruse, R.: Foundations on Neuro-Fuzzy Systems. Wiley, Chichester (1997)

    Google Scholar 

  2. Pal, S.K., Mitra, S.: Neuro-Fuzzy Pattern Recognition. J. Wiley, New York (1999)

    Google Scholar 

  3. Duch, W., Diercksen, G.H.F.: Feature Space Mapping as a universal adaptive system. Computer Physics Communic. 87, 341–371 (1995)

    Article  MATH  Google Scholar 

  4. Duch, W.: Similarity based methods: a general framework for classification, approximation and association. Control and Cybernetics 29, 937–968 (2000)

    MATH  MathSciNet  Google Scholar 

  5. Duch, W., Adamczak, R., Diercksen, G.H.F.: Classification, Association and Pattern Completion using Neural Similarity Based Methods. Applied Mathematics and Computer Science 10, 101–120 (2000)

    Google Scholar 

  6. Duch, W., Jankowski, N.: New neural transfer functions. Neural Computing Surveys 2, 639–658 (1999)

    Google Scholar 

  7. Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)

    MATH  MathSciNet  Google Scholar 

  8. Golub, T.R., et al.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)

    Article  Google Scholar 

  9. Duch, W., Adamczak, R., Grabczewski, K.: A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. Trans. on Neural Networks 12, 277–306 (2001)

    Article  Google Scholar 

  10. Grąbczewski, K., Duch, W.: Heterogeneous forests of decision trees. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 504–509. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcin Blachnik
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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

Publish with us

Policies and ethics

Search

Navigation