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Induction of Decision Trees Based on the Rough Set Theory

  • Tu Bao Ho
  • Trong Dung Nguyen
  • Masayuki Kimura
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

This paper aimed at two following objectives. One was the introduction of a new measure (R-measure) of dependency between groups of attributes in a data set, inspired by the notion of dependency of attribute in the rough set theory. The second was the application of this measure to the problem of attribute selection in decision tree induction, and an experimental comparative evaluation of decision tree systems using R-measure and other different attribute selection measures most of them are widely used in machine learning: gain-ratio, gini-index, d N distance, relevance, x 2.

Keywords

Cross Validation Attribute Selection Selection Measure Pruning Technique Experimental Comparative Study 
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 Japan 1998

Authors and Affiliations

  • Tu Bao Ho
    • 1
  • Trong Dung Nguyen
    • 1
  • Masayuki Kimura
    • 1
  1. 1.Japan Advanced Institute of Science and TechnologyHokuriku Tatsunokuchi, IshikawaJapan

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