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Characteristics of Accuracy and Coverage in Rule Induction

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

Rough set analysis are closely related with accuracy and coverage. However, there have been few studies on the formal characteristics of accuracy and coverage for rule induction have never been discussed until Tsumoto showed several characteristics of accuracy and coverage. In this paper, the following characteristics of accuracy and coverage are further investigated: (1) The higher the accuracy of the conjunctive formula become, the lower the effect on the conjunction will become. (2) Coverage will decrease more rapidly than accuracy. (3) The change of coverage becomes very small when the length of the conjunctive formula becomes larger. (4) The discussions above are corresponding to those on sensitivity and specificity. (5) When we focus on accurate classification, the classification efficiency, which is the product of sensitivity and specificity will become lower.

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References

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Author information

Authors and Affiliations

  1. Department of Medical Informatics, Shimane Medical University, School of Medicine, Enya-cho Izumo City, Shimane, 693-8501, Japan

    Shusaku Tsumoto

Authors
  1. Shusaku Tsumoto
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2003 Springer-Verlag Berlin Heidelberg

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Tsumoto, S. (2003). Characteristics of Accuracy and Coverage in Rule Induction. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_30

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  • DOI: https://doi.org/10.1007/3-540-39205-X_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

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