文献类型：期刊文献

英文题名：COLLABORATIVE OPTIMIZATION OF MULTICLASS IMBALANCED LEARNING: DENSITY-AWARE AND REGION-GUIDED BOOSTING

作者：Li, Chuantao[1,2]; Li, Zhi[1]; Xu, Jiahao[1]; Li, Jie[1]; Li, Sheng[1]

机构：[1] School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, 524088, China; [2] School of Automation Engineering, University of Electronic Science and Technology of China, Sichuan, Chengdu, 611731, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20260009691)

语种：英文

外文关键词：Adaptive boosting - Density (specific gravity) - Learning systems - Optimization

外文摘要：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. Copyright ? 2025, The Authors. All rights reserved.