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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2000: Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents pp 139–144Cite as

A Heuristic Optimal Reduct Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

Abstract

Reduct finding, especially optimal reduct finding, similar to feature selection problem, is a crucial task in rough set applications to data mining, In this paper, we propose a heuristic reduct finding algorithm, which is based on frequencies of attributes appeared in discernibility matrix. Our method does not guarantee to find optimal reduct, but experiment shows that in most situations it does; and it is very fast.

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References

  1. J.W. Guan, D.A. Bell. Rough computational methods for information systems, Artificial intellignence, 105(1998)77–103

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Author information

Authors and Affiliations

  1. Computer Science Department, Tsinghua University, 100084, Beijing, P.R.China

    Keyun Hu, Lili Diao, Yuchang Lu & Chunyi Shi

Authors
  1. Keyun Hu
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  2. Lili Diao
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  3. Yuchang Lu
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  4. Chunyi Shi
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Kwong Sak Leung  & Lai-Wan Chan  & 

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

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© 2000 Springer-Verlag Berlin Heidelberg

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Cite this paper

Hu, K., Diao, L., Lu, Y., Shi, C. (2000). A Heuristic Optimal Reduct Algorithm. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_21

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  • DOI: https://doi.org/10.1007/3-540-44491-2_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

  • eBook Packages: Springer Book Archive

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