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Evolved Fuzzy Min-Max Neural Network for Unknown Labeled Data and its Application on Defect Recognition in Depth

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Abstract

Pattern classification is one of the most important issue in the data-driven application domains. Unlike the traditional unlabeled data, unknown labeled data refers to the testing data that cannot be classified into the existed category in this paper. How to learn the unknown labeled data is a crucial issue in the data classification. In this paper, an evolved fuzzy min-max neural network for unknown labeled data classification (FMM-ULD) is proposed. In FMM-ULD, the unknown labeled data handling process is designed. Moreover, in the unknown labeled data handling process, a decision function and a threshold function are designed. In addition, FMM-ULD can realize further correction for the unsatisfactory data classification of the known category. The experimental results using UCI benchmark data set show that FMM-ULD get good performance for handling the unknown labeled data as a general method. In addition, the application result on the pipeline defect recognition in depth shows that FMM-ULD is effective in handling the real-application unknown labeled data problem.

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Acknowledgements

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

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Authors and Affiliations

  1. School of Renewable Energy, Shenyang Institute of Engineering, Shenyang, 110136, Liaoning, China

    Yanjuan Ma & Yan Zhao

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

    Jinhai Liu

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  1. Yanjuan Ma
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  2. Jinhai Liu
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  3. Yan Zhao
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Correspondence to Yanjuan Ma.

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Ma, Y., Liu, J. & Zhao, Y. Evolved Fuzzy Min-Max Neural Network for Unknown Labeled Data and its Application on Defect Recognition in Depth. Neural Process Lett 53, 85–105 (2021). https://doi.org/10.1007/s11063-020-10377-7

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