Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting
This work addresses multiclass imbalanced learning for machine learning applications, representing an incremental advancement with a novel integration of existing concepts.
The paper tackles classification bias from class imbalance by proposing a collaborative optimization boosting model that integrates density and confidence factors, achieving significant performance improvements over eight state-of-the-art baselines on 20 public datasets.
Numerous studies attempt to mitigate classification bias caused by class imbalance. However, existing studies have yet to explore the collaborative optimization of imbalanced learning and model training. This constraint hinders further performance improvements. To bridge this gap, this study proposes a collaborative optimization Boosting model of multiclass imbalanced learning. This model is simple but effective by integrating the density factor and the confidence factor, this study designs a noise-resistant weight update mechanism and a dynamic sampling strategy. Rather than functioning as independent components, these modules are tightly integrated to orchestrate weight updates, sample region partitioning, and region-guided sampling. Thus, this study achieves the collaborative optimization of imbalanced learning and model training. Extensive experiments on 20 public imbalanced datasets demonstrate that the proposed model significantly outperforms eight state-of-the-art baselines. The code for the proposed model is available at: https://github.com/ChuantaoLi/DARG.