Double-kernel based class-specific broad learning system for multiclass imbalance learning

摘要

Imbalance learning has gained more and more attention from researchers. Most of the efforts so far have focused on binary imbalance problems, while there are numerous unresolved multiclass imbalance problems in real-world scenarios. The diversity of data distribution and the poor performance of traditional multiclass classification algorithms present significant challenges for classifying multiclass imbalanced data. This paper proposes a double kernel-based class-specific broad learning system (DKCSBLS) for multi-class imbalance learning. Class-specific penalty coefficients are incorporated into the model to increase the focus on minority classes. Moreover, double kernel mapping mechanism is designed to extract more robust features. Extensive experiments on various real-world datasets demonstrate the superiority of our proposed approach.