arXiv Machine Learning
Jun 16

Imbalanced Classification under Capacity Constraints

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.

By Daniel Fraiman, Ricardo Fraiman
arXiv AI
2d ago

Partial AUC Maximization from Positive-unlabeled Data

arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarize...

By Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Hiroshi Takahashi, Kazuki Adachi, Yasuhiro Fujiwara
arXiv Machine Learning
Sep 11

AUC Maximization from Biased Positive-unlabeled Data with Confidence

The paper introduces a method for maximizing the area under the receiver operating characteristic curve (AUC) when only biased positive and unlabeled (PU) data are available. It leverages confidence scores—probabilities that an instance is positive—associated with a small set of labeled positives to derive an AUC risk estimator that accounts for bias. Experiments on eight real-world datasets demonstrate the method’s effectiveness.

By Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara
arXiv Machine Learning
5d ago

Tabular Imbalanced Learning: A Survey, Benchmark, and Practical Guide

The paper surveys tabular imbalanced learning and introduces TILBench, a benchmark evaluating over 40 methods on 57 datasets. It presents a unified taxonomy of approaches and shows that no single method dominates across all settings, with performance depending on dataset regimes and computational constraints. Practical recommendations for method selection and future research directions are provided.

By Ruizhe Liu, Jiaqi Luo