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International Conference on Rough Sets and Knowledge Technology

RSKT 2011: Rough Sets and Knowledge Technology pp 63–70Cite as

An Efficient Fuzzy-Rough Attribute Reduction Approach

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

Abstract

Fuzzy rough set method provides an effective approach to data mining and knowledge discovery from hybrid data including categorical values and numerical values. However, its time-consumption is very intolerable to analyze data sets with large scale and high dimensionality. In this paper, we propose a strategy to improve a heuristic process of fuzzy-rough feature selection. Experiments show that this modified algorithm is much faster than its original version. It is worth noting that the performance of the modified algorithm becomes more visible when dealing with larger data sets.

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Author information

Authors and Affiliations

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

    Yuhua Qian, Chao Li & Jiye Liang

Authors
  1. Yuhua Qian
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  2. Chao Li
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  3. 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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Qian, Y., Li, C., Liang, J. (2011). An Efficient Fuzzy-Rough Attribute Reduction Approach. 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_11

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  • DOI: https://doi.org/10.1007/978-3-642-24425-4_11

  • 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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