Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling
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...
| Authors | Yuxin Liu, Xiong Jin, Yang Han |
|---|---|
| Primary category | stat.ME |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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