Skip to main content
SpringerLink
Log in
Book cover

International Fuzzy Systems Association World Congress

IFSA 2003: Fuzzy Sets and Systems — IFSA 2003 pp 319–327Cite as

Imprecise Modelling Using Gradual Rules and Its Application to the Classification of Time Series

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2715))

Abstract

This paper presents an alternative to precise analytical modelling, by means of imprecise interpolative models. The model specification is based on gradual rules that express constraints that govern the interpolation mechanism. The modelling strategy is applied to the classification of time series. In this con-text, it is shown that good recognition performance can be obtained with models that are highly imprecise.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D. Dubois, H. Prade, “Gradual inference rules in approximate reasoning”, Information Sciences, Vol. 61, no 1–2, pp. 103–122, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  2. D. Dubois, H. Prade, L. Ughetto, “Checking the Coherence and Redundancy of Fuzzy Knowledge Bases”, IEEE Trans. on Fuzzy Systems, Vol. 5, no 3, pp. 398–417, 1997.

    Article  Google Scholar 

  3. Galichet S., Dubois D., Prade H., “Imprecise specification of ill-known functions using gradual rules”, Proc. of the 2nd Int. Workshop on Hybrid Methods for Adaptive Systems (EUNITE.02), Albufeira, Portugal, pp. 512–520, Sept. 2002.

    Google Scholar 

  4. Galichet S., Dubois D., Prade H., “Fuzzy interpolation and level 2 gradual rules”, submitted to EUSFLAT Conference, Zittau, Germany, Sept. 2003.

    Google Scholar 

  5. E. Keogh, S. Kasetty, “On the need for time series data mining benchmarks: a survey and empirical demonstration”, Proc. of the 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Edmonton, Canada, pp. 102–111, July 2002.

    Google Scholar 

  6. E. Keogh, H. Hochheiser, B. Shneiderman, “An augmented visual query mechanism for finding patterns in time series data”, Proc. of the 5th Int. Conf. on Flexible Query Answering Systems (FQAS 2002), Copenhagen, Denmark, pp. 240–250, Oct. 2002.

    Google Scholar 

  7. A. Nanopoulos, R. Alcock, Y. Manolopoulos, “Feature-based classification of time-series data”, Int. Journal of Computer Research, Vol. 10, no 3, 2001.

    Google Scholar 

  8. A. Sala, P. Albertos, “Inference error minimisation: fuzzy modelling of ambiguous functions”, Fuzzy Sets and Systems, Vol. 121, no 1, pp. 95–111, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  9. The UCI KDD Archive (http://kdd.ics.uci.edu/), University of California, Irvine.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse (IRIT), Université Paul Sabatier, 118, route de Narbonne, 31062, Toulouse Cedex 4

    Sylvie Galichet, Didier Dubois & Henri Prade

  2. LISTIC, Université de Savoie, 41, avenue de la Plaine, BP 806, 74016, Annecy Cedex

    Sylvie Galichet

Authors
  1. Sylvie Galichet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Didier Dubois
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henri Prade
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey

    Taner Bilgiç

  2. Dept. of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000, Gent, Belgium

    Bernard De Baets

  3. Department of Electrical and Electronics Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey

    Okyay Kaynak

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Galichet, S., Dubois, D., Prade, H. (2003). Imprecise Modelling Using Gradual Rules and Its Application to the Classification of Time Series. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_38

Download citation

  • DOI: https://doi.org/10.1007/3-540-44967-1_38

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40383-8

  • Online ISBN: 978-3-540-44967-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation