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International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 552–565Cite as

Rough Set Analysis of Classification Data with Missing Values

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Abstract

In this paper, we consider a rough set analysis of non-ordinal and ordinal classification data with missing attribute values. We show how this problem can be addressed by several variants of Indiscernibility-based Rough Set Approach (IRSA) and Dominance-based Rough Set Approach (DRSA). We propose some desirable properties that a rough set approach being able to handle missing attribute values should possess. Then, we analyze which of these properties are satisfied by the considered variants of IRSA and DRSA.

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References

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Acknowledgment

The first author acknowledges financial support from the Poznań University of Technology, grant no. 09/91/DSMK/0609.

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Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Marcin Szeląg, Jerzy Błaszczyński & Roman Słowiński

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

Authors
  1. Marcin Szeląg
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  2. Jerzy Błaszczyński
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  3. Roman Słowiński
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Corresponding author

Correspondence to Marcin Szeląg .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Szeląg, M., Błaszczyński, J., Słowiński, R. (2017). Rough Set Analysis of Classification Data with Missing Values. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_44

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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