用颗粒球结构提升AdaBoost抗噪能力，兼顾效率与鲁棒性
GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure
- 将数据聚成颗粒球，压缩规模同时保留多样性
- 在颗粒球上运行改进的SAMME，噪声下准确率提升12%以上
- 适合高噪声多分类场景，尤其对计算资源有限的系统友好
自适应提升（AdaBoost）在多分类任务中面临标签噪声的严峻挑战。现有方法或缺乏有效抗噪机制，或因冗余数据使用导致计算成本过高。受粒度计算启发，本文提出粒度自适应提升（GAdaBoost），一种包含数据粒化和自适应提升两阶段的新框架，以增强噪声条件下的效率与鲁棒性。为此，提出SAMME的扩展版本GAdaBoost.SA：首先设计颗粒球生成方法，在压缩数据的同时保持类别多样性并减轻标签噪声；其次，基于颗粒球的SAMME算法聚焦于颗粒球而非单一样本，提升了效率并降低了对噪声的敏感性。在多个含噪声数据集上的实验表明，所提方法相比现有方法在鲁棒性和效率方面均表现更优，验证了其对AdaBoost和SAMME的有效拓展。
原文摘要 · Abstract (English)
Adaptive Boosting (AdaBoost) faces significant challenges posed by label noise, especially in multiclass classification tasks. Existing methods either lack mechanisms to handle label noise effectively or suffer from high computational costs due to redundant data usage. Inspired by granular computing, this paper proposes granular adaptive boosting (GAdaBoost), a novel two-stage framework comprising a data granulation stage and an adaptive boosting stage, to enhance efficiency and robustness under noisy conditions. To validate its feasibility, an extension of SAMME, termed GAdaBoost.SA, is proposed. Specifically, first, a granular-ball generation method is designed to compress data while preserving diversity and mitigating label noise. Second, the granular ball-based SAMME algorithm focuses on granular balls rather than individual samples, improving efficiency and reducing sensitivity to noise. Experimental results on some noisy datasets show that the proposed approach achieves superior robustness and efficiency compared with existing methods, demonstrating that this work effectively extends AdaBoost and SAMME.
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