Difference Similitude Method in Knowledge Reduction

  • Ming Wu
  • Delin Xia
  • Puliu Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


An intergraded reduction method, which includes attributes reduction and rules induction, is proposed in this context. Firstly, U/C is calculated for reducing the complexity of the reduction. Then, difference and similitude sets, of the reduced information system, are calculated. The last, the attributes are selected according to their abilities for giving high accurate rules. The time complexity of the reduction, including attributes reduction and rules induction, is O(∣C∣2∣U/C∣2).


Time Complexity Rule Induction Feature Subset Selection Conditional Attribute Discernibility Matrix 
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  1. 1.
    Blum Avrim, L., Pat, L.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97(1-2), 245–271 (1997)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)MATHCrossRefGoogle Scholar
  3. 3.
    Guyon, I., Andre, E.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)MATHCrossRefGoogle Scholar
  4. 4.
    Michalski, R.S., Chilausky, R.L.: Learning by being told and learning from examples: An experimental comparison of two methods of knowledge acquisition in context of developing on expert system for soybean disease diagnosm. Policy Analysis and Information Systems 4(2), 125–150 (1980)Google Scholar
  5. 5.
    Quinlan, J.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  6. 6.
    Liu, H., Setiono, R.: Dimensionality reduction via discretization. Knowledge-Based Systems 9(1), 67–72 (1996)CrossRefGoogle Scholar
  7. 7.
    Zdzislaw, P.: Rough sets. International Journal of Parallel Programming 11(5), 341–356 (1982)MATHGoogle Scholar
  8. 8.
    Hu, X.H., Nick, C.: Learning in relational databases: A rough set approach. International Journal of Computational Intelligence 11(2), 323–338 (1995)Google Scholar
  9. 9.
    Ji-Ye, L., Zong-Ben, X.: The algorithm on knowledge reduction in incomplete information systems. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 10(1), 95–103 (2002)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of applications and advances of rough set theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ming Wu
    • 1
  • Delin Xia
    • 1
  • Puliu Yan
    • 1
  1. 1.School of Electronic InformationWuhan UniversityP.R. China

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