🤖 AI Summary
Label noise in supervised classification can severely degrade model performance, necessitating label-cleaning methods that require no prior knowledge of the noise characteristics. This work proposes an unsupervised noise identification framework that constructs data subsets via Bernoulli random sampling and leverages the linear relationship between cross-validation error and subset noise level to build a mixture distribution for distinguishing clean and noisy samples. Theoretically, we prove that under this sampling scheme, the mean noise level converges to two separable distributions, offering the first probabilistic coupling analysis guarantee for unsupervised label cleaning. The method is classifier-agnostic and consistently achieves significant improvements in both noise detection accuracy and downstream classification performance across synthetic and real-world datasets.
📝 Abstract
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.