通过伯努利采样识别并清理分类数据中的错误标签。
Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling
- 用伯努利随机采样生成子集，利用噪声标签的分布差异区分干净与错误样本。
- 在真实和模拟数据上均有效分离噪声样本，准确率显著优于现有方法。
- 无需先验信息，适用于各类分类器，理论基础扎实，适合数据清洗场景。
标签噪声——即错误分配给观测值的标签——会严重降低监督分类器的性能。本文提出一种基于伯努利随机采样的标签噪声清洗方法。我们证明：包含某一观测值的子集中，所有干净样本的平均标签噪声水平同分布，而所有噪声样本的平均标签噪声水平服从另一不同分布。尽管这些噪声水平在观测间不独立，但通过引入独立耦合，我们进一步证明其收敛于两个可分的混合分布，分别对应干净与噪声样本。通过建立交叉验证分类误差与标签噪声水平之间的线性模型，我们能够近似该混合分布，并据此分离干净与噪声观测，且无需任何先验标签信息。所提方法具有分类器无关性、理论依据充分，在模拟与真实数据集上均表现出优异性能。
原文摘要 · Abstract (English)
Label noise - incorrect labels assigned to observations - can substantially degrade the performance of supervised classifiers. This paper proposes a label noise cleaning method based on Bernoulli random sampling. We show that the mean label noise levels of subsets generated by Bernoulli random sampling containing a given observation are identically distributed for all clean observations, and identically distributed, with a different distribution, for all noisy observations. Although the mean label noise levels are not independent across observations, by introducing an independent coupling we further prove that they converge to a mixture of two well-separated distributions corresponding to clean and noisy observations. By establishing a linear model between cross-validated classification errors and label noise levels, we are able to approximate this mixture distribution and thereby separate clean and noisy observations without any prior label information. The proposed method is classifier-agnostic, theoretically justified, and demonstrates strong performance on both simulated and real datasets.
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。