Incremental Rules Induction Based on Rule Layers

  • Shusaku Tsumoto
  • Shoji Hirano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7414)


This paper proposes a new framework for incremental learning based on accuracy and coverage. Classification of addition of example into four cases gives two inequalities for accuracy and coverage. The proposed method classifies a set of formulae into three layers: rule layer, subrule layer and non-rule layer by using the inequalities obtained. Then, subrule layer plays a central role in updating rules. The proposed method was evaluated on a dataset on meningitis, whose results show that it outperforms other conventional rule induction methods.


incremental rule induction rough sets accuracy coverage subrule layer 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shusaku Tsumoto
    • 1
  • Shoji Hirano
    • 1
  1. 1.Department of Medical Informatics, School of Medicine, Faculty of MedicineShimane UniversityIzumoJapan

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