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International Conference on Rough Sets and Knowledge Technology

RSKT 2011: Rough Sets and Knowledge Technology pp 8–15Cite as

Uncertainty and Feature Selection in Rough Set Theory

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6954))

Abstract

In rough set theory, the uncertainty of granulation and efficient feature selection algorithms have attracted much attention in recent years. We focus on the review of several common uncertainty measures and the relationships among them. An efficient accelerator is developed to accelerate a heuristic process of feature selection.

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Author information

Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Jiye Liang

Authors
  1. Jiye Liang
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, S4S 0A2, Saskatchewan, Canada

    JingTao Yao

  2. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Canada

    Sheela Ramanna

  3. Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, P.R. China

    Guoyin Wang

  4. Chair of Computer Science, University of Rzeszów, Poland

    Zbigniew Suraj

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© 2011 Springer-Verlag Berlin Heidelberg

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Liang, J. (2011). Uncertainty and Feature Selection in Rough Set Theory. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-24425-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24424-7

  • Online ISBN: 978-3-642-24425-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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