Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling
This addresses label noise issues in supervised classification, offering a classifier-agnostic solution, though it appears incremental as it builds on existing noise cleaning approaches.
The paper tackles the problem of label noise degrading supervised classifier performance by proposing a Bernoulli random sampling-based cleaning method that separates clean and noisy observations without prior label information, demonstrating strong performance on simulated and real datasets.
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.