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Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and “Do Not Care” Conditions

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Rough Sets (IJCRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9920))

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Abstract

In this paper we present results of novel experiments conducted on 12 data sets with many missing attribute values interpreted as attribute-concept values and “do not care” conditions. In our experiments complexity of rule sets, in terms of the number of rules and the total number of conditions induced from such data, are evaluated. The simpler rule sets are considered better. Our first objective was to check which interpretation of missing attribute values should be used to induce simpler rule sets. There is some evidence that the “do not care” conditions are better. Our secondary objective was to test which of the three probabilistic approximations: singleton, subset or concept, used for rule induction should be used to induce simpler rule sets. The best choice is the subset probabilistic approximation and the singleton probabilistic approximation is the worst choice.

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Author information

Authors and Affiliations

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

    Patrick G. Clark, Cheng Gao & Jerzy W. Grzymala-Busse

  2. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225, Rzeszow, Poland

    Jerzy W. Grzymala-Busse

Authors
  1. Patrick G. Clark
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  2. Cheng Gao
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  3. Jerzy W. Grzymala-Busse
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Corresponding author

Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. Universidad Católica del Norte, Antofagasta, Chile

    Víctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del Católica del Norte, Antofagasta, Chile

    Claudio Meneses

  5. Tongji University, Shanghai, China

    Duoqian Miao

  6. University of Applied Sciences, Munich, Germany

    Georg Peters

  7. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  8. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  9. Universidad de Chile, Santiago, Chile

    Richard Weber

  10. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

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Clark, P.G., Gao, C., Grzymala-Busse, J.W. (2016). Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and “Do Not Care” Conditions. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-47160-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47159-4

  • Online ISBN: 978-3-319-47160-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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