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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)

Abstract

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.

Keywords

Decision Tree Classification Accuracy Leaf Node Information Entropy Decision Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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