A VPRSM based approach for inducing decision trees

  • Authors:

  • Shuqin Wang;Jinmao Wei;Junping You;Dayou Liu

  • Affiliations:

  • School of Mathematics & Statistics, Northeast, Normal University, Changchun, Jilin, China;Institute of Computational Intelligence, Northeast Normal University, Changchun, Jilin, China;Institute of Computational Intelligence, Northeast Normal University, Changchun, Jilin, China;Open Symbol Computation and Knowledge Engineering Laboratory of, State Education, Jilin University, Changchun, Jilin, China

  • Venue:
  • RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
  • Year:
  • 2006

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Abstract

This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model(VPRSM). From the Rough Set theory point of view, in the process of inducing decision trees, some methods, such as information entropy based methods, emphasize the effect of class distribution. The more unbalanced the class distribution is, the more favorable it is. Whereas the Rough Set based approaches for inducing decision trees emphasize the effect of certainty. The more certain it is, the better it is. Two main concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced and discussed in the paper. The comparison between the presented approach and C4.5 on some data sets from the UCI Machine Learning Repository is also reported