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Rough Set Approaches to Imprecise Modeling

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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 the classical rough set approaches, lower approximations of single decision classes have been mainly treated. Based on those approximations, attribute reduction and rule induction have been developed. In this paper, from the authors’ recent studies, we demonstrate that various analyses are conceivable by treating lower approximations of unions of multiple decision classes.

This work was partially supported by JSPS KAKENHI Grant Number 26350423.

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Author information

Authors and Affiliations

  1. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531, Japan

    Masahiro Inuiguchi

Authors
  1. Masahiro Inuiguchi
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Corresponding author

Correspondence to Masahiro Inuiguchi .

Editor information

Editors and Affiliations

  1. Universidad CatĂłlica del Norte, Antofagasta, Chile

    VĂ­ctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del CatĂłlica 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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Inuiguchi, M. (2016). Rough Set Approaches to Imprecise Modeling. 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_5

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  • DOI: https://doi.org/10.1007/978-3-319-47160-0_5

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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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