Skip to main content
SpringerLink
Log in

Order Based Genetic Algorithms for the Search of Approximate Entropy Reducts

  • Conference paper
  • First Online:
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

We use entropy to extend the rough set based notion of a reduct. We show that the order based genetic algorithms, applied to the search of classical decision reducts, can be used in exactly the same way in case of extracting optimal approximate entropy reducts from data.

Supported by Polish National Committee for Scientific Research (KBN) grant No. 8T11C02519, as well as the Research Centre of PJIIT.

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Davis, L. (ed.): Handbook of Genetic Algorithms. Van Nostrand Reinhold (1991).

    Google Scholar 

  2. Duentsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106 (1998) pp. 77–107.

    Article  MathSciNet  Google Scholar 

  3. Kapur, J., Kesavan, H.: Entropy Optimization Principles with Applications. Academic Press (1992).

    Google Scholar 

  4. Pawlak, Z.: Rough sets — Theoretical aspects of reasoning about data (1991).

    Google Scholar 

  5. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann (1988).

    Google Scholar 

  6. Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proc. of IMLC’98 (1998).

    Google Scholar 

  7. Rissanen J.: Minimum-description-length principle. In: S. Kotz, N.L. Johnson (eds), Encyclopedia of Statistical Sciences. Wiley (1985) pp. 523–527.

    Google Scholar 

  8. Ślęzak, D.: Approximate reducts in decision tables. In: Proc. of IPMU’96 (1996).

    Google Scholar 

  9. Ślęzak, D.: Approximate decision reducts (in Polish). Ph.D. thesis, Institute of Mathematics, Warsaw University (2001).

    Google Scholar 

  10. Ślęzak, D.: Approximate Entropy Reducts. Accepted to Fundamenta Informaticae.

    Google Scholar 

  11. Wróblewski, J.: Theoretical Foundations of Order-Based Genetic Algorithms. Fundamenta Informaticae 28/3–4 (1996) pp. 423–430.

    MATH  MathSciNet  Google Scholar 

  12. Wróblewski, J.: Adaptive methods of object classification (in Polish). Ph.D. thesis, Institute of Mathematics, Warsaw University (2001).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Canada

    Dominik Ślęzak

  2. Polish-Japanese Institute of Information Technology, Warsaw, Poland

    Dominik Ślęzak & Jakub Wróblewski

Authors
  1. Dominik Ślęzak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jakub Wróblewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ślęzak, D., Wróblewski, J. (2003). Order Based Genetic Algorithms for the Search of Approximate Entropy Reducts. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_45

Download citation

  • DOI: https://doi.org/10.1007/3-540-39205-X_45

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation