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Double-kernelized weighted broad learning system for imbalanced data

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Abstract

Broad learning system (BLS) is an emerging neural network with fast learning capability, which has achieved good performance in various applications. Conventional BLS does not effectively consider the problems of class imbalance. Moreover, parameter tuning in BLS requires much effort. To address the challenges mentioned above, we propose a double-kernelized weighted broad learning system (DKWBLS) to cope with imbalanced data classification. The double-kernel mapping strategy is designed to replace the random mapping mechanism in BLS, resulting in more robust features while avoiding the step of adjusting the number of nodes. Furthermore, DKWBLS considers the imbalance problem and achieves more explicit decision boundaries. Numerous experimental results show the superiority of DKWBLS in tackling imbalance problems over other imbalance learning approaches.

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Funding

This article was funded by Key-Area Research and Development Program of Guangdong Province under the Grant No. (2019B010153002) and National Natural Science Foundation of China under the Grant No. (62106224) and Marine Economy Development (Six Marine Industries) Special Foundation of Department of Natural Resources of Guangdong Province under Grant GDNRC [2020]056.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Panyu district, Guangzhou, 510006, Guangdong, China

    Wuxing Chen & Weiwen Zhang

  2. State Key Laboratory of Industrial Control Technology, Zhejiang University, Yuquan district, Hangzhou, 310012, Zhejiang, China

    Kaixiang Yang

  3. Engineering Institute, Huaqiao University, Fengze District, Quanzhou, 362021, Fujian, China

    Yifan Shi

  4. School of Computer Science and Engineering, South China University of Technology, Panyu district, Guangzhou, 510006, Guangdong, China

    Zhiwen Yu

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  1. Wuxing Chen
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Correspondence to Kaixiang Yang.

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Chen, W., Yang, K., Zhang, W. et al. Double-kernelized weighted broad learning system for imbalanced data. Neural Comput & Applic 34, 19923–19936 (2022). https://doi.org/10.1007/s00521-022-07534-5

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