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Robust Feature Selection Based on Fuzzy Rough Sets with Representative Sample

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Book cover Advanced Data Mining and Applications (ADMA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11888))

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Abstract

Fuzzy rough set theory is not only an objective mathematical tool to deal with incomplete and uncertain information but also a powerful computing paradigm to realize feature selection. However, the existing fuzzy rough set models are sensitive to noise in feature selection. To solve this problem, a novel fuzzy rough set model that is robust to noise is studied in this paper, which expands the research of fuzzy rough set theory and broadens the application of feature selection. In this study, we propose a fuzzy rough set model with representative sample (RS-FRS), and it deals better with noise. Firstly, the fuzzy membership of the sample is defined, and it is added into the construction of RS-FRS model, which could increase the upper and lower approximation of RS-FRS and reduce the influence of the noise samples. The proposed model considers the fuzziness of the sample membership degree, and it can approximate other subsets of the domain space with the fuzzy equivalent approximation space more precisely. Furthermore, RS-FRS model does not need to set parameters for the model in advance, which helps reduce the model complexity and human intervention effectively. At the same time, we also give a careful study to the related properties of RS-FRS model, and a robust feature selection based on RS-FRS with sample pair selection is designed. Extensive experiments are given to illustrate the robustness and effectiveness of the proposed model.

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Acknowledgement

This work was supported by Shandong Province Key Research and Development Plan (2018GSF118133) and China Postdoctoral Science Foundation (2018M642144).

Author information

Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250100, China

    Zhimin Zhang & Shoushui Wei

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    Zhimin Zhang, Weitong Chen & Feng Liu

  3. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Chengyu Liu & Yuwen Li

  4. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Yun Kang

Authors
  1. Zhimin Zhang
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  2. Weitong Chen
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  3. Chengyu Liu
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  4. Yun Kang
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  5. Feng Liu
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  6. Yuwen Li
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  7. Shoushui Wei
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Corresponding authors

Correspondence to Yuwen Li or Shoushui Wei .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  2. The University of Queensland, St. Lucia, QLD, Australia

    Sen Wang

  3. Flinders University, Bedford Park, SA, Australia

    Shaowen Qin

  4. Dalian Neusoft University of Information, Dalian, China

    Xue Li

  5. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Zhang, Z. et al. (2019). Robust Feature Selection Based on Fuzzy Rough Sets with Representative Sample. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-35231-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35230-1

  • Online ISBN: 978-3-030-35231-8

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