Double-kernelized weighted broad learning system for imbalanced data

被引：0
作者：
Wuxing Chen
Kaixiang Yang
Weiwen Zhang
Yifan Shi
Zhiwen Yu
机构：
[1] Guangdong University of Technology,School of Computer Science and Technology
[2] Zhejiang University,State Key Laboratory of Industrial Control Technology
[3] Huaqiao University,Engineering Institute
[4] South China University of Technology,School of Computer Science and Engineering
来源：
Neural Computing and Applications | 2022年 / 34卷
关键词：
Broad learning system; Imbalance learning; Kernel learning; Binary classification;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：19923 / 19936
页数：13
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