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Induction of positive and negative deterministic rules based on rough set model

  • Communications Session 3B Learning and Discovery Systems
  • Conference paper
  • First Online:
Foundations of Intelligent Systems (ISMIS 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1325))

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Abstract

One of the most important problems on rule induction methods is that they cannot extract rules which plausibly represent negative information on experts' decision, processes. This paper first discusses the characteristics of two measures, classification accuracy and coverage and shows that accuracy and coverage are measures of both positive and negative rules, respectively. Then, an algorithm for induction of positive and negative rules is introduced. The proposed method is evaluated on medical databases, the experimental results of which show that induced rules correctly represent experts' knowledge.

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References

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Authors and Affiliations

  1. Department of Information Medicine Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima, 113, Bunkyo-ku Tokyo, Japan

    Shusaku Tsumoto

Authors
  1. Shusaku Tsumoto
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Editor information

Zbigniew W. Raś Andrzej Skowron

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© 1997 Springer-Verlag Berlin Heidelberg

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Tsumoto, S. (1997). Induction of positive and negative deterministic rules based on rough set model. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_29

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  • DOI: https://doi.org/10.1007/3-540-63614-5_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63614-4

  • Online ISBN: 978-3-540-69612-4

  • eBook Packages: Springer Book Archive

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