Science in China Series F: Information Sciences

, Volume 50, Issue 2, pp 188–197

A general approach to attribute reduction in rough set theory



The concept of a consistent approximation representation space is introduced. Many types of information systems can be treated and unified as consistent approximation representation spaces. At the same time, under the framework of this space, the judgment theorem for determining consistent attribute set is established, from which we can obtain the approach to attribute reductions in information systems. Also, the characterizations of three important types of attribute sets (the core attribute set, the relative necessary attribute set and the unnecessary attribute set) are examined.


rough sets attribute reduction information systems approximation representation spaces 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pawlak Z. Rough sets. Int J Comp Inf Sci, 1982, 11: 341–356MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Pawlak Z. Rough Sets — Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Publishers, 1991MATHGoogle Scholar
  3. 3.
    Kryszkiewicz M. Comparative study of alternative types of knowledge reduction in insistent systems. Int J Intel Syst, 2001, 16: 105–120MATHCrossRefGoogle Scholar
  4. 4.
    Zhang W-X, Leung Y, Wu W-Z. Information Systems and Knowledge Discovery. Beijing: Science Press, 2003Google Scholar
  5. 5.
    Beynon M. Reducts within the variable precision rough sets model: A further investigation. Eur J Oper Res, 2001, 134: 592–605MATHCrossRefGoogle Scholar
  6. 6.
    Zhang W-X, Mi J-S, Wu W-Z. Approaches to knowledge reductions in inconsistent systems. Int J Intel Syst, 2003, 18: 989–1000MATHCrossRefGoogle Scholar
  7. 7.
    Qiu G-F, Li H-Z, Xu L-D, et al. A knowledge processing method for intelligent systems based on inclusion degree. Expert Syst, 2003, 20(4): 187–195CrossRefGoogle Scholar
  8. 8.
    Mi J-S, Wu W-Z, Zhang W-X. Approaches to knowledge reduction based on variable precision rough set model. Inf Sci, 2004, 159: 255–272MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Zhang M, Wu W-Z. Knowledge reduction in information systems with fuzzy decisions. J Eng Math, 2003, 20(2): 53–58MATHGoogle Scholar
  10. 10.
    Leung Y, Wu W-Z, Zhang W-X. Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res, 2006, 168(1): 164–180MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Wu W-Z, Zhang M, Li H-Z, et al. Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Inf Sci, 2005, 174(3–4): 143–164MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Skowron A, Rauszwer C. The discernibility matrices and functions in information systems. In: Slowinski R, ed. Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory. Dordrecht: Kluwer Academic Publishers, 1992. 331–362Google Scholar

Copyright information

© Science in China Press 2007

Authors and Affiliations

  1. 1.Institute for Information and System Sciences, Faculty of ScienceXi’an Jiaotong UniversityXi’anChina
  2. 2.School of ManagementXi’an University of Architecture and TechnologyXi’anChina
  3. 3.Information CollegeZhejiang Ocean UniversityZhoushanChina

Personalised recommendations