On Reduct Construction Algorithms

  • Yiyu Yao
  • Yan Zhao
  • Jue Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4062)


This paper critically analyzes reduct construction methods at two levels. At a high level, one can abstract commonalities from the existing algorithms, and classify them into three basic groups based on the underlying control structures. At a low level, by adopting different heuristics or fitness functions for attribute selection, one is able to derive most of the existing algorithms. The analysis brings new insights into the problem of reduct construction, and provides guidelines for the design of new algorithms.


Reduct construction algorithms deletion strategy addition-deletion strategy addition strategy attribute selection heuristics 


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  1. 1.
    Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88 (2000)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Hu, X., Cercone, N.: Learning in relational databases: a rough set approach. Computation Intelligence: An International Journal 11, 323–338 (1995)Google Scholar
  4. 4.
    Jenson, R., Shen, Q.: A rough set-aided system for sorting WWW bookmarks. In: Zhong, N., et al. (eds.) Web Intelligence: Research and Development, pp. 95–105 (2001)Google Scholar
  5. 5.
    Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159, 255–272 (2004)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Miao, D., Wang, J.: An information representation of the concepts and operations in rough set theory. Journal of Software 10, 113–116 (1999)Google Scholar
  7. 7.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Boston (1991)MATHGoogle Scholar
  8. 8.
    Shen, Q., Chouchoulas, A.: A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Engineering Applications of Artificial Intelligence 13, 263–278 (2000)CrossRefGoogle Scholar
  9. 9.
    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, Dordrecht (1992)Google Scholar
  10. 10.
    Slezak, D.: Various approaches to reasoning with frequency based decision reducts: a survey. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough set methods and applications, pp. 235–285. Physica-verlag, Heidelberg (2000)Google Scholar
  11. 11.
    Wang, G., Yu, H., Yang, D.: Decision table reduction based on conditional information entropy. Chinese Journal of Computers 25, 759–766 (2002)MathSciNetGoogle Scholar
  12. 12.
    Wang, J., Wang, J.: Reduction algorithms based on discernibility matrix: the ordered attributes method. Journal of Computer Science and Technology 16, 489–504 (2001)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Yu, H., Yang, D., Wu, Z., Li, H.: Rough set based attribute reduction algorithm. Computer Engineering and Applications 17, 22–47 (2001)Google Scholar
  14. 14.
    Zhao, K., Wang, J.: A reduction algorithm meeting users’ requirements. Journal of Computer Science and Technology 17, 578–593 (2002)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Ziarko, W.: Rough set approaches for discovering rules and attribute dependencies. In: Klösgen, W., Żytkow, J.M. (eds.) Handbook of Data Mining and Knowledge Discovery, Oxford, pp. 328–339 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yiyu Yao
    • 1
  • Yan Zhao
    • 1
  • Jue Wang
    • 2
  1. 1.Department of Computer ScienceUniversity of ReginaRegina, SaskatchewanCanada
  2. 2.Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingChina

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