Skip to main content
Springer Nature Link
Log in

Scalable Feature Selection Using Rough Set Theory

  • Conference paper
  • First Online:
Rough Sets and Current Trends in Computing (RSCTC 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2005))

Included in the following conference series:

  • 5131 Accesses

Abstract

In this paper, we address the problem of feature subset selection using rough set theory. We propose a scalable algorithm to find a set of reducts based on discernibility function, which is an alternative solution for the exhaustive approach. Our study shows that our algorithm improves the classical one from three points of view: computation time, reducts size and the accuracy of induced model.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bazan G., Skowron A., Synak P.: Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables, ICS Research Report 43/94, Warsaw Univ. of Tech., (1994).

    Google Scholar 

  2. Boussouf M.: A Hybrid Approach to Feature Selection. In Proceedings of the 2nd European Symposium, PKDD'98, (1998) 230–238.

    Google Scholar 

  3. Fayyad U.M., Irani K.B.: Multi-interval Discretization of Continuous-attributes for classification learning IJCAF93, (1993) 1022–1027.

    Google Scholar 

  4. Kohavi, R., Frasca, B.: Useful feature subset and rough sets reducts. In Proceedings of the 3rd Int. Workshop on Rough Sets and Soft Computing, (1994) 310–317.

    Google Scholar 

  5. Modrzejewski, M.: Feature selection using rough sets theory. In Proceedings of the ECML, (1993) 213–226.

    Google Scholar 

  6. Liu, H., Setiono, R.: A probabilistic approach for feature selection: A filter solution. In 13th International Conference on Machine Learning (ICML'96), (1996) 319–327.

    Google Scholar 

  7. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht, The Netherlands (1991).

    MATH  Google Scholar 

  8. Pawlak, Z.: Rough Sets: present state and the future. Foundations of Computing and Decision Sciences, 18(3-4), (1993) 157–166.

    MATH  MathSciNet  Google Scholar 

  9. Quafafou, M.: a-RST: A generalization of rough set theory. In Proceedings in the Fifth International Workshop on Rough Sets and Soft Computing, RSSC'97. (1997).

    Google Scholar 

  10. Quafafou, M., Boussouf, M.: Induction of Strong Feature Subsets. In 1st European Symposium, PKDD'97, (1997) 384–392.

    Google Scholar 

  11. Quafafou M., Boussouf, M., (1999): Generalized Rough Sets based Feature Selection. Intelligent Data Analysis Journal, (IDA) 4(1), (1999).

    Google Scholar 

  12. Quinlan J. R.: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, (1993).

    Google Scholar 

  13. Skowron A. and Rauszer C: The Discernibility Matrice and Functions in Information Systems. Intelligent Decision Support. Handbook of Applications and Advances of Rough Sets Theory. Dordrecht: Kluwer. (1992) 331–362.

    Google Scholar 

  14. Ziarko W.: Anaalysis on Uncertain Information in the Framework of Variable Precision Rough Sets. Foundations of Computing and Decision Sciences, 18(3-4), (1993) 381–396.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IRIN, Université de Nantes, 2 rue de la Houssiniére, BP 92208 - 44322, Nantes Cedex 03, France

    Moussa Boussouf & Mohamed Quafafou

Authors
  1. Moussa Boussouf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Quafafou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Wojciech Ziarko  & Yiyu Yao  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Boussouf, M., Quafafou, M. (2001). Scalable Feature Selection Using Rough Set Theory. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_15

Download citation

  • DOI: https://doi.org/10.1007/3-540-45554-X_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43074-2

  • Online ISBN: 978-3-540-45554-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics