A New Approach for Selecting Attributes Based on Rough Set Theory

  • Jiang Yun
  • Li Zhanhuai
  • Zhang Yang
  • Zhang Qiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)


Decision trees are widely used in data mining and machine learning for classification. In the process of constructing a tree, the criteria of selecting partitional attributes will influence the classification accuracy of the tree. In this paper, we present a new concept, weighted mean roughness, which is based on rough set theory, for choosing attributes. The experimental result shows that compared with the entropy-based approach, our approach is a better way to select nodes for constructing decision trees.


Decision Tree Classification Accuracy Leaf Node Information Entropy Decision Table 
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  1. 1.
    Ross Quinlan, J.: C4.5: Program for Machine Learning. Morgan Kaufmann, San Francisco (1992)Google Scholar
  2. 2.
    Liu, B., Hsu, W., Ma, Y.: Intergrating Classification and Association Rule Mining. In: Proc. KDD (1998)Google Scholar
  3. 3.
    Buntine, W.L., Weigend, A.S.: Computing Second Derivatives in Feed-forward Networks: A Review. IEEE Transactions on Neural Networks 5, 480–488 (1991)CrossRefGoogle Scholar
  4. 4.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge Press, Cambridge (2000)Google Scholar
  5. 5.
    Pawlak, Z.W.: Rough Sets. International Journal of Information and Computer Science 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Pawlak, Z.W.: Rough Sets and Intelligent Data Analysis. Information sciences 147, 1–12 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Beynon, M.: Reducts within the Variable Precision Rough Set Model: A Further Investigation European. Journal of Operational Research 134, 592–605 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Murphy, P., Aha, W.: UCI Repository of Machine Learning Databases (1996),
  9. 9.
    Hu, X., Cercone, N.: Data Mining Via Generalization, Discretization and Rough Set Feature Selection. Knowledge and Information System: An International Journal 1 (1999)Google Scholar
  10. 10.
    Zhou, Z.-H.: AI Softwares&Codes (Maintained by Zhi-Hua Zhou), 2004-02-29

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jiang Yun
    • 1
    • 2
  • Li Zhanhuai
    • 1
  • Zhang Yang
    • 1
  • Zhang Qiang
    • 2
  1. 1.College of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.College of Mathematics and Information ScienceNorthwest Normal UniversityLanzhouChina

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