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Semi-supervised fuzzy broad learning system based on mean-teacher model

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Abstract

Fuzzy broad learning system (FBLS) is a newly proposed fuzzy system, which introduces Takagi–Sugeno fuzzy model into broad learning system. It has shown that FBLS has better nonlinear fitting ability and faster calculation speed than the most of fuzzy neural networks proposed earlier. At the same time, compared to other fuzzy neural networks, FBLS has fewer rules and lower cost of training time. However, label errors or missing are prone to appear in large-scale dataset, which will greatly reduce the performance of FBLS. Therefore, how to use limited label information to train a powerful classifier is an important challenge. In order to address this problem, we introduce Mean-Teacher model for the fuzzy broad learning system. We use the Mean-Teacher model to rebuild the weights of the output layer of FBLS, and use the Teacher–Student model to train FBLS. The proposed model is an implementation of semi-supervised learning which integrates fuzzy logic and broad learning system in the Mean-Teacher-based knowledge distillation framework. Finally, we have proved the great performance of Mean-Teacher-based fuzzy broad learning system (MT-FBLS) through a large number of experiments.

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Data availability

We made use of publicly available datasets.

Notes

  1. http://archive.ics.uci.edu/ml/datasets.html.

  2. https://github.com/PengC98/MT-FBLS.git.

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Funding

This work was supported in part by the Natural Science Foundation of China (61991401, U20A20189), in part by the National Key Research and Development Project (2020YFB1710003), in part by the Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System (JETRCNGDSS202206), in part by the Quzhou Science and Technology Plan Project (2023K265), and in part by the Engineering Research Center of Ministry of Education for Nuclear Technology Application (HISTYB2022-7).

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Authors and Affiliations

  1. School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 201306, China

    Zizhu Fan

  2. Key Laboratory of Advanced Control and Optimization of Jiangxi Province, East China Jiaotong University, Nanchang, 330013, China

    Yijing Huang & Chao Xi

  3. School of Mechanical and Electronic Engineering, East China University of Technology, Nanchang, 330013, China

    Chao Xi

  4. Department of Engineering, King’s College London, London, 4616, UK

    Cheng Peng

  5. School of Digital Media, Jiangnan University, Wuxi, 214122, China

    Shitong Wang

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  1. Zizhu Fan
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  4. Cheng Peng
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  5. Shitong Wang
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Correspondence to Chao Xi or Cheng Peng.

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Fan, Z., Huang, Y., Xi, C. et al. Semi-supervised fuzzy broad learning system based on mean-teacher model. Pattern Anal Applic 27, 18 (2024). https://doi.org/10.1007/s10044-024-01217-8

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