Adaptive weights-based relaxed broad learning system for imbalanced classification

As a shallow neural network, broad learning system (BLS) has gained significant attention in both academia and industry due to its efficiency and effectiveness. However, BLS and its variants are suboptimal when confronted with imbalanced data scenarios. Firstly, strict binary labeling strategy hinders effective disparities between different classes. Secondly, they generally do not distinguish between the contributions of minority and majority classes, resulting in classification outcomes biased toward the majority classes. To address these deficiencies, we propose an adaptive weights-based relaxed broad learning system for handling imbalanced classification tasks. We provide a label relaxation technique to construct a novel label matrix that not only widens the margins between classes but also maintains label consistency within each class. Additionally, an adaptive weighting strategy assigns higher weights to minority samples based on density information within and between classes. This enables the model to learn a more discriminative transformation matrix for imbalanced classification. The alternating direction method of multipliers algorithm is employed to solve the resulting model. Experimental results on numerous public imbalanced data sets demonstrate the effectiveness and efficiency of the proposed method.