Skip to main content

A General Definition of an Attribute Reduct

  • Conference paper
Rough Sets and Knowledge Technology (RSKT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4481))

Included in the following conference series:

Abstract

A reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of a given information table. A general definition of an attribute reduct is presented. Specifically, we discuss the following issues: First, there are a variety of properties that can be observed in an information table. Second, the preservation of a certain property by an attribute set can be evaluated by different measures, defined as different fitness functions. Third, by considering the monotonicity property of a particular fitness function, the reduct construction method needs to be carefully examined. By adopting different heuristics or fitness functions for preserving a certain property, one is able to derive most of the existing definitions of a reduct. The analysis brings new insight into the problem of reduct construction, and provides guidelines for the design of new algorithms.

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 39.99
Price excludes VAT (USA)
  • Available as 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beaubouef, T., Petry, F.E., Arora, G.: Information-theoretic measures of uncertainty for rough sets and rough relational databases. Information Sciences 109, 185–195 (1998)

    Article  Google Scholar 

  2. Beynon, M.: Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134, 592–605 (2001)

    Article  MATH  Google Scholar 

  3. Hu, Q., Yu, D., Xie, Z.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters 27, 414–423 (2006)

    Article  Google Scholar 

  4. Pawlak, Z.: Rough sets. International Journal of Computer Information and Science 11, 341–356 (1982)

    Article  MathSciNet  Google Scholar 

  5. Qiu, G.F., Zhang, W.X., Wu, W.Z.: Charaterization of attributes in generalized approximation representation spaces. In: Urzyczyn, P. (ed.) TLCA 2005. LNCS, vol. 3461, pp. 84–93. Springer, Heidelberg (2005)

    Google Scholar 

  6. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  7. Swiniarski, R.W.: Rough sets methods in feature reduction and classification. International Journal of Applied Mathematics and Computer Science 11, 565–582 (2001)

    MathSciNet  Google Scholar 

  8. Wang, G.Y., Zhao, J., Wu, J.: A comparitive study of algebra viewpoint and information viewpoint in attribute reduction. Foundamenta Informaticae 68, 1–13 (2005)

    MathSciNet  Google Scholar 

  9. Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. International Journal of Man-machine Studies 37, 793–809 (1992)

    Article  Google Scholar 

  10. Yao, Y.Y., Zhao, Y.: Conflict analysis based on discernibility and indiscernibility. In: Proceedings of 2007 IEEE Symposium on Foundations of Computational Intelligence (2007)

    Google Scholar 

  11. Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

JingTao Yao Pawan Lingras Wei-Zhi Wu Marcin Szczuka Nick J. Cercone Dominik Ślȩzak

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Zhao, Y., Luo, F., Wong, S.K.M., Yao, Y. (2007). A General Definition of an Attribute Reduct. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72458-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72457-5

  • Online ISBN: 978-3-540-72458-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics