A New Rule Induction Method from a Decision Table Using a Statistical Test

  • Tsukasa Matsubayashi
  • Yuichi Kato
  • Tetsuro Saeki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7414)


Rough Sets theory provides a method of estimating and/or inducing knowledge structure of if-then rules from various databases, using approximations of accuracy and coverage indices. Several recent studies have examined the confidence of these indices. In these studies their estimated rules were based on a sample data set obtained from a population, and the sampling affects the confidence of the estimation. However, these studies of the quality of the approximation evaluate the effects on rule estimation indirectly. In this paper, we propose a new rule induction method by statistical testing which directly contains the effect of sampling. The validity and usefulness of our method are confirmed by a computer simulation experiment and comparison of the results with those by other well-known methods.


Decision Table Rule Induction Coverage Index Discernibility Matrix Rule Estimation 
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 2012

Authors and Affiliations

  • Tsukasa Matsubayashi
    • 1
  • Yuichi Kato
    • 1
  • Tetsuro Saeki
    • 2
  1. 1.Shimane UniversityMatsue cityJapan
  2. 2.Yamaguchi UniversityUbe cityJapan

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