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ComEnVprs: A Novel Approach for Inducing Decision Tree Classifiers

  • Shuqin Wang
  • Jinmao Wei
  • Junping You
  • Dayou Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

This paper presents a new approach for inducing decision trees by combining information entropy criteria with VPRS based methods. From the angle of rough set theory, when inducing decision trees, entropy based methods emphasize the effect of class distribution. Whereas the rough set based approaches emphasize the effect of certainty. The presented approach takes the advantages of both criteria for inducing decision trees. Comparisons between the presented approach and the fundamental information entropy based method on some data sets from the UCI Machine Learning Repository are also reported.

Keywords

Decision Tree Classification Error Information Entropy Node Count Average Classification Accuracy 
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 2006

Authors and Affiliations

  • Shuqin Wang
    • 1
  • Jinmao Wei
    • 2
    • 3
  • Junping You
    • 2
  • Dayou Liu
    • 3
  1. 1.School of Mathematics & StatisticsNortheast Normal UniversityJilinChina
  2. 2.Institute of Computational IntelligenceNortheast Normal UniversityJilinChina
  3. 3.Open Symbol Computation and Knowledge Engineering Laboratory of State EducationJilin UniversityJilinChina

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