提出协同优化框架，提升多类别不平衡学习的分类效果。
Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting
- 融合密度与置信度因子，设计抗噪权重更新机制。
- 在20个公开数据集上显著优于8个主流基线方法。
- 适合处理类别分布极不均衡的工业级分类任务。
众多研究致力于缓解类别不平衡带来的分类偏差，但现有方法尚未探索不平衡学习与模型训练的协同优化，这一局限制约了性能进一步提升。为此，本文提出一种多类别不平衡学习的协同优化增强模型。该模型通过集成密度因子与置信度因子，设计了抗噪权重更新机制和动态采样策略。各模块并非独立运行，而是紧密协作，共同实现权重更新、样本区域划分与区域引导采样，从而实现不平衡学习与模型训练的协同优化。在20个公开不平衡数据集上的大量实验表明，所提模型显著优于8个最先进的基线方法。代码已开源：https://github.com/ChuantaoLi/DARG。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。