arXiv Machine Learning By Daniel Fraiman, Ricardo Fraiman

Imbalanced Classification under Capacity Constraints

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arXiv:2605. 03289v2 Announce Type: replace-cross Abstract: Detecting observations from a minority class under severe class imbalance is a central challenge in applications such as fraud detection, medical screening, and industrial quality control.

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arXiv Statistics ML
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Robust performance metrics for imbalanced classification problems

The paper demonstrates that common binary classification metrics—Matthews' correlation coefficient, Cohen's κ, the F-score, and the Jaccard similarity—are not robust to extreme class imbalance, as the Bayes classifier’s true positive rate tends to zero when the minority class proportion vanishes. To address this, the authors propose robustified versions of these metrics that include a tuning parameter, ensuring that the Bayes-optimal classifier’s threshold remains bounded and its true positive rate stays above zero even in highly imbalanced scenarios. The study provides theoretical bounds, simulation results, and practical guidance on applying these robust metrics to real data, such as a credit‑default dataset, and discusses their relationship to ROC and precision‑recall curves.

By Hajo Holzmann, Bernhard Klar
arXiv Machine Learning
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Focused PU learning from imbalanced data

arXiv:2605.14467v2 Announce Type: replace Abstract: We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as dise...

By Elias Zavitsanos, Georgios Paliouras