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Increasing Data Set Incompleteness May Improve Rule Set Quality

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Software and Data Technologies (ICSOFT 2008)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 47))

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Abstract

This paper presents a new methodology to improve the quality of rule sets. We performed a series of data mining experiments on completely specified data sets. In these experiments we removed some specified attribute values, or, in different words, replaced such specified values by symbols of missing attribute values, and used these data for rule induction while original, complete data sets were used for testing. In our experiments we used the MLEM2 rule induction algorithm of the LERS data mining system, based on rough sets. Our approach to missing attribute values was based on rough set theory as well. Results of our experiments show that for some data sets and some interpretation of missing attribute values, the error rate was smaller than for the original, complete data sets. Thus, rule sets induced from some data sets may be improved by increasing incompleteness of data sets. It appears that by removing some attribute values, the rule induction system, forced to induce rules from remaining information, may induce better rule sets.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, U.S.A.

    Jerzy W. Grzymala-Busse

  2. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymala-Busse

  3. Touchnet Information Systems, Inc., Lenexa, KS, 66219, U.S.A.

    Witold J. Grzymala-Busse

Authors
  1. Jerzy W. Grzymala-Busse
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  2. Witold J. Grzymala-Busse
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Editor information

Editors and Affiliations

  1. Control and Communication (INSTICC), Institute for Systems and Technologies of Information,, Rua do Vale de Chaves, Estefanilha, 2910-761, Setúbal, Portugal

    José Cordeiro

  2. Interdisciplinary Institute for Collaboration and Research on Enterprise Systems and Technology – IICREST, P.O. Box 104, 1618, Sofia, Bulgaria

    Boris Shishkov

  3. INSTICC, Avenida D. Manuel I, 2910, Setúbal, Portugal

    AlpeshKumar Ranchordas

  4. School of Computing, Dublin City University, Dublin 9, Ireland

    Markus Helfert

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Grzymala-Busse, J.W., Grzymala-Busse, W.J. (2009). Increasing Data Set Incompleteness May Improve Rule Set Quality. In: Cordeiro, J., Shishkov, B., Ranchordas, A., Helfert, M. (eds) Software and Data Technologies. ICSOFT 2008. Communications in Computer and Information Science, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05201-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-05201-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05200-2

  • Online ISBN: 978-3-642-05201-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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