Double-kernelized weighted broad learning system for imbalanced data

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.

Automated summary

Broad Learning System (BLS) is a fast learning neural network that has shown good performance in various applications. However, conventional BLS has limitations in dealing with class imbalance and parameter tuning. To overcome these challenges, we propose a double-kernelized weighted broad learning system (DKWBLS) that improves feature representation and addresses class imbalance. Experimental results demonstrate the superiority of DKWBLS in handling imbalanced data.