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On Attribute Reduction of Rough Set Based on Pruning Rules

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Rough Set and Knowledge Technology (RSKT 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6401))

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Abstract

Combining the concept of attribute dependence and attribute similarity in rough sets, the pruning ideas in the attribute reduction was proposed, the estimate method and fitness function in the processing of reduction was designed, and a new reduction algorithm based on pruning rules was developed, the complexity was analyzed, furthermore, many examples was given. The experimental results demonstrate that the developed algorithm can got the simplest reduction.

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Author information

Authors and Affiliations

  1. Key laboratory of knowledge processing and networked manufacturing, college of hunan province, Xiangtan, 411201, China

    Hongyuan Shen, Shuren Yang & Jianxun Liu

  2. Institute of Information and Electrical Engineering, Hunan University of Science and technology, Xiangtan, 411201, China

    Hongyuan Shen & Shuren Yang

Authors
  1. Hongyuan Shen
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  2. Shuren Yang
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  3. Jianxun Liu
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Editor information

Editors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China

    Jian Yu

  2. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  3. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, Nova Scotia, Canada

    Pawan Lingras

  4. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Guoyin Wang

  5. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2010 Springer-Verlag Berlin Heidelberg

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Shen, H., Yang, S., Liu, J. (2010). On Attribute Reduction of Rough Set Based on Pruning Rules. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-16248-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16247-3

  • Online ISBN: 978-3-642-16248-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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