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Parameter-Free Extreme Learning Machine for Imbalanced Classification

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Abstract

Imbalanced data distribution is a common problem in classification situations, that is the number of samples in different categories varies greatly, thus increasing the classification difficulty. Although many methods have been used for the imbalanced data classification, there are still problems with low classification accuracy in minority class and adding additional parameter settings. In order to increase minority classification accuracy in imbalanced problem, this paper proposes a parameter-free weighting learning mechanism based on extreme learning machine and sample loss values to balance the number of samples in each training step. The proposed method mainly includes two aspects: the sample weight learning process based on the sample losses; the sample selection process and weight update process according to the constraint function and iterations. Experimental results on twelve datasets from the KEEL repository show that the proposed method could achieve more balanced and accurate results than other compared methods in this work.

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Acknowledgements

This article was funded by the National Study Abroad Fund.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Li Li, Kaiyi Zhao, Ruizhi Sun & Gang Yuan

  2. Scientific Research Base for Integrated Technologies of Precision Agriculture (Animal Husbandry), The Ministry of Agriculture, Beijing, 100083, China

    Ruizhi Sun

  3. School of Natural and Computational Sciences, Massey University, Auckland, 0745, New Zealand

    Jiangzhang Gan & Tong Liu

Authors
  1. Li Li
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  2. Kaiyi Zhao
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  3. Ruizhi Sun
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  4. Jiangzhang Gan
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  5. Gang Yuan
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  6. Tong Liu
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Correspondence to Ruizhi Sun.

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Li, L., Zhao, K., Sun, R. et al. Parameter-Free Extreme Learning Machine for Imbalanced Classification. Neural Process Lett 52, 1927–1944 (2020). https://doi.org/10.1007/s11063-020-10282-z

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