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A Comparison of Rule Matching Methods Used in AQ15 and LERS

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Foundations of Intelligent Systems (ISMIS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1932))

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Abstract

This paper focuses on a performance comparison of two rule matching (classification) methods, used in data mining systems AQ15 and LERS. All rule sets used in our experiments were induced by the LERS (Learning from Examples using Rough Sets) system from ten typical input data sets. Then these rule sets were truncated using three different criteria: t-weight, u-weight and the strongest rule. The truncation process was performed using six different cut-off values for tweight, six different cut-off values for u-weight and using the strongest rule option. Hence for each of the input rule files thirteen truncated rule sets were created. Performance was measured by a classification error rate. The objective of this study was to determine the best overall method of classification and the best truncation option.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, KS 66045, Lawrence, USA

    Jerzy W. Grzymala-Busse

  2. Systems Material Handling Co., KS 66062, Olathe, USA

    Pankaj Shah

Authors
  1. Jerzy W. Grzymala-Busse
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  2. Pankaj Shah
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, 01-237, Warsaw, Poland

    Zbigniew W. Raś

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Department of Science and Technology, Waseda University, 61-414, 3-4-1, Ohkubo, Shinjuku-ku, 169-8555, Tokyo, Japan

    Setsuo Ohsuga

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© 2000 Springer-Verlag Berlin Heidelberg

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Grzymala-Busse, J.W., Shah, P. (2000). A Comparison of Rule Matching Methods Used in AQ15 and LERS. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_16

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  • DOI: https://doi.org/10.1007/3-540-39963-1_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41094-2

  • Online ISBN: 978-3-540-39963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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