提出抗标签噪声的最小最大提升方法，兼顾理论保障与实际性能。
Robust Minimax Boosting with Performance Guarantees
- 通过最小化最坏情况误差概率设计鲁棒提升框架
- 在有限样本下提供逼近无噪声最优误差的理论保证
- 对各类标签噪声均有效，适合噪声数据场景
提升方法通常具有优异分类精度，但在标签噪声存在时可能出现显著性能下降。现有鲁棒提升方法虽对特定类型噪声提供理论鲁棒性保证，且仅表现出中等性能退化，但其理论结果未考虑现实噪声类型及有限训练规模，且在无噪声情况下表现仍不理想。本文提出鲁棒最小最大提升（RMBoost）方法，旨在最小化最坏情况误差概率，并对一般类型标签噪声具有鲁棒性。此外，我们给出了RMBoost在有限样本下的性能保证：相对于无噪声情形的误差，以及相对于最佳可能误差（贝叶斯风险）。实验结果表明，RMBoost不仅对标签噪声具有强韧性，还能实现高分类准确率。
原文摘要 · Abstract (English)
Boosting methods often achieve excellent classification accuracy, but can experience notable performance degradation in the presence of label noise. Existing robust methods for boosting provide theoretical robustness guarantees for certain types of label noise, and can exhibit only moderate performance degradation. However, previous theoretical results do not account for realistic types of noise and finite training sizes, and existing robust methods can provide unsatisfactory accuracies, even without noise. This paper presents methods for robust minimax boosting (RMBoost) that minimize worst-case error probabilities and are robust to general types of label noise. In addition, we provide finite-sample performance guarantees for RMBoost with respect to the error obtained without noise and with respect to the best possible error (Bayes risk). The experimental results corroborate that RMBoost is not only resilient to label noise but can also provide strong classification accuracy.
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。