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An Attribute Reduction P System Based on Rough Set Theory

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

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Abstract

Attribute reduction is an important issue in rough set theory. Many heuristic algorithms have been proposed to compute the minimal attribute reduction since it is a NP hard problem, while most of them have the drawback to fall into local optimal solution. The other way to solve this problem is based on parallel computing. Membrane computing model is a distributed, maximal parallel and non-deterministic computing model inspired from cell. In this paper, we attempt to solve the attribute reduction problem by membrane computing, and propose a cell-like P system \(\varPi _{AR}\) to compute all exact minimal attribute reductions with \(\mathrm{O}(m \log n)\) time complexity.

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Author information

Authors and Affiliations

  1. Colllege of Computer Science, Chongqing University, Chongqing, China

    Ping Guo & Junqi Xiang

Authors
  1. Ping Guo
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  2. Junqi Xiang
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Corresponding author

Correspondence to Ping Guo .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Guo, P., Xiang, J. (2018). An Attribute Reduction P System Based on Rough Set Theory. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_18

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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