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Evolved fuzzy min-max neural network for new-labeled data classification

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Abstract

Pattern classification is a fundamental problem in many data-driven application domains. New-labeled data refers to the data with the labels that are new and different from source labels. How to learn the new-labeled data is a crucial research in the data classification. In this paper, an evolved fuzzy min-max neural network for new-labeled data classification (FMM-NLA) is proposed. In FMM-NLA, the network can be self-evolved. Unlike the traditional FMM methods, the trained network of FMM-NLA can be expanded when new-labeled data added. FMM-NLA is a continuing-learning method, which can realize the continuing training process without retraining all the data. In order to verify the superiority of the proposed method, benchmark data sets are used. The experimental results show that FMM-NLA is effective in handling new-labeled data. Moreover, the application result on the pipeline defect recognition in depth shows that FMM-NLA is effective in solving the new-labeled defect recognition problem.

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Acknowledgements

This work was supported by National Key R&D Program of China (2017YFF0108800), the National Natural Science Foundation of China (61973071, 61627809, 61703087), the Liaoning Natural Science Foundation of China (2019 − KF − 03 − 04), the Liaoning Revitalization Talents Program(XLYC1907138).

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Authors and Affiliations

  1. Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, School of Renewable Energy, Shenyang Institute of Engineering, Shenyang, 110136, China

    Yanjuan Ma

  2. State Key Laboratory of Synthetical Automation for Process Industries, College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Jinhai Liu, Fuming Qu & Hongfei Zhu

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  1. Yanjuan Ma
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  2. Jinhai Liu
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  3. Fuming Qu
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  4. Hongfei Zhu
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Correspondence to Yanjuan Ma or Jinhai Liu.

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Ma, Y., Liu, J., Qu, F. et al. Evolved fuzzy min-max neural network for new-labeled data classification. Appl Intell 52, 305–320 (2022). https://doi.org/10.1007/s10489-021-02259-9

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