arXiv Machine Learning
Sep 3

Bayes-Optimal BER and AUC: Estimation and Evaluation of Estimators

The paper introduces soft‑label‑based estimators for the Bayes‑optimal balanced error rate (BER) and area under the ROC curve (AUC), extending from a clean setting with known class priors to a realistic scenario with unknown priors and corrupted soft labels. It also adapts the FeeBee evaluation framework to assess these estimators without needing the true optimum, providing practical evaluation scores for any estimator of optimal BER or AUC. Experiments on synthetic and real datasets confirm the effectiveness of both the estimators and the evaluation method.

By Ryota Ushio, Takashi Ishida, Masashi Sugiyama
arXiv Machine Learning
Aug 24

Benchmarking noisy label detection methods

arXiv:2510.16211v2 Announce Type: replace Abstract: Label noise is a common problem in real-world datasets, affecting both model training and validation. Clean data are essential for achieving strong...

By Henrique Pickler, Jorge K. S. Kamassury, Danilo Silva