Skip to main content

A New Version of Rough Set Exploration System

  • Conference paper
  • First Online:
Rough Sets and Current Trends in Computing (RSCTC 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2475))

Included in the following conference series:

Abstract

We introduce a new version of the Rough Set Exploration System - a software tool featuring a library of methods and a graphical user interface supporting variety of rough-set-based computations. Methods, features and abilities of the implemented software are discussed and illustrated with a case study in data analysis.

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

Access this chapter

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. Bay, S.D.: Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets. In: Proc. of 15 ICML, Morgan Kaufmann, Madison, 1998

    Google Scholar 

  2. Bazan J., A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables, In Polkowski L.(ed.), Rough Sets in Knowledge Discovery, Physica Verlag, Heidelberg, 1998 [10], vol. 1, pp. 321–365

    Google Scholar 

  3. Bazan, J.G., Nguyen, H.S., Nguyen, S.H, Synak, P., Wróblewski, J.: Rough Set Algorithms in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds), Rough Set Methods and Applications, Physica-Verlag, 2000 pp. 49–88.

    Google Scholar 

  4. Bazan J., Szczuka M. RSES and RSESlib-A Collection of Tools for Rough Set Computations, Proc. of RSCTC’2000, LNAI 2005, Springer Verlag, Berlin, 2001

    Google Scholar 

  5. Garey M., Johnson D., Computers and Intarctability: A Guide to the Theory of NP-completness, W.H. Freeman&Co., San Francisco, 1998, (twentieth print)

    Google Scholar 

  6. GrzymaIla-Busse J., A New Version of the Rule Induction System LERS Fundamenta Informaticae, Vol. 31(1), 1997, pp. 27–39

    Google Scholar 

  7. Nguyen Sinh Hoa, Nguyen Hung Son, Discretization Methods in Data Mining, In Polkowski L.(ed.), Rough Sets in Knowledge Discovery, Physica Verlag, Heidelberg, 1998 [10] vol. 1, pp. 451–482

    Google Scholar 

  8. Hoa S. Nguyen, A. Skowron and P. Synak, Discovery of Data Patterns with Applications to Decomposition and Classfification Problems. In Polkowski L.(ed.), Rough Sets in Knowledge Discovery, Physica Verlag, Heidelberg, 1998 [10] vol. 2, pp. 55–97.

    Google Scholar 

  9. Michie D., Spiegelhalter D. J., Taylor C. C., Machine Learning, Neural and Statistical Classification, Ellis Horwood, London, 1994

    MATH  Google Scholar 

  10. Skowron A., Polkowski L. (ed.), Rough Sets in Knowledge Discovery vol. 1 and 2, Physica Verlag, Heidelberg, 1998

    Google Scholar 

  11. Ślęzak D., Wróblewski J., Classification Algorithms Based on Linear Combinations of Features. In: Proc. of PKDD’99. LNAI 1704, Springer Verlag, Berlin, 1999, pp. 548–553.

    Google Scholar 

  12. Wróblewski J., Covering with Reducts-A Fast Algorithm for Rule Generation, Proceeding of RSCTC’98, LNAI 1424, Springer Verlag, Berlin, 1998, pp. 402–407

    Google Scholar 

  13. Wróblewski J.: Ensembles of classifiers based on approximate reducts, Fundamenta Informaticae 47(3,4), IOS Press (2001) 351–360.

    MATH  MathSciNet  Google Scholar 

  14. Bazan J., Szczuka M., The RSES Homepage, http://alfa.mimuw.edu.pl/~rses

  15. Ørn A., The ROSETTA Homepage, http://www.idi.ntnu.no/~aleks/rosetta

  16. Blake C.L., Merz C.J., UCI Repository of machine learning databases, Irvine, CA: University of California, 1998, http://www.ics.uci.edu/~mlearn

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bazan, J.G., Szczuka, M.S., Wróblewski, J. (2002). A New Version of Rough Set Exploration System. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_52

Download citation

  • DOI: https://doi.org/10.1007/3-540-45813-1_52

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44274-5

  • Online ISBN: 978-3-540-45813-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics