Skip to main content
SpringerLink
Log in

Classification of Interval Information with Data Drift

  • Conference paper
  • First Online:
Modeling and Using Context (CONTEXT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9405))

Abstract

The paper deals with the classification task of interval information, when processed data is gradually displaced, i.e. they originate from a nonstationary environment. The procedure worked out is characterized by its many practical properties: ensuring the minimum expected value of misclassifications; allowing influence on the probability of errors in classification to particular classes; reducing patterns by eliminating elements with insignificant or negative influence on the results’ accuracy, enabling an unlimited number of patterns and their shapes. The appropriate modifications of the classifier not only lead to an increase in the effectiveness of the procedure, but above all adapt to data drift.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and 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

References

  1. Duda, R.O., Hart, P.E., Storck, D.G.: Pattern Classification. Wiley, New York (2001)

    Google Scholar 

  2. Kelley, C.T.: Iterative Methods for Optimization. SIAM, Philadelphia (1999)

    Book  MATH  Google Scholar 

  3. Krasotkina, O.V., Mottl, V.V., Turkov, P.A.: Bayesian approach to the pattern recognition problem in nonstationary environment. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 24–29. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Kulczycki, P.: Estymatory jadrowe w analizie systemowej. WNT, Warsaw (2005)

    Google Scholar 

  5. Kulczycki, P., Hryniewicz, O., Kacprzyk, J. (eds.): Techniki informacyjne w badaniach systemowych. WNT, Warsaw (2007)

    Google Scholar 

  6. Kulczycki, P., Kowalski, P.A.: Bayes classification of imprecise information of interval type. Control Cybern. 40(1), 101–123 (2011)

    MathSciNet  MATH  Google Scholar 

  7. Kulczycki, P., Kowalski, P.A.: Bayes classification for nonstationary patterns. Int. J. Comput. Methods 12(2), 19 (2015). Article ID 1550008

    Article  MathSciNet  MATH  Google Scholar 

  8. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)

    Book  MATH  Google Scholar 

  9. Venter, G.: Review of optimization techniques. In: Blockley, R., Shyy, W. (eds.) Encyclopedia of Aerospace Engineering, pp. 5229–5238. Wiley, New York (2010)

    Google Scholar 

  10. Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, London (1995)

    Book  MATH  Google Scholar 

  11. Zhao,Y., He, Q., Chen, Q.: An interval set classification based on support vector machines. In: 2nd International Conference on Networking and Services, Silicon Valley, USA, 25–30 September 2005, pp. 81–86 (2005)

    Google Scholar 

  12. Zurada, J.: Introduction to Artificial Neural Neural Systems. West Publishing, St. Paul (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Piotr Kulczycki & Piotr A. Kowalski

Authors
  1. Piotr Kulczycki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr A. Kowalski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piotr Kulczycki .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Roskilde University, Roskilde, Denmark

    Henning Christiansen

  2. ENS-Pavillon Jardin, CNRS-Institut Jean-Nicod, Paris, France

    Isidora Stojanovic

  3. Department of Computer Science, University of Cyprus, Aglantzia, Cyprus

    George A. Papadopoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Kulczycki, P., Kowalski, P.A. (2015). Classification of Interval Information with Data Drift. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25591-0_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25590-3

  • Online ISBN: 978-3-319-25591-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation