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...
The paper investigates binary classification with abstention under separate class‑conditional error constraints, aiming to minimize abstention while keeping both error types below specified thresholds. It derives the distribution‑free minimax rate of excess abstention risk, introduces surrogate‑loss formulations for computational feasibility with models like neural networks, and provides finite‑sample guarantees for excess surrogate ambiguity risk. The authors also formulate the learning task as a constrained optimization problem, analyze its computational complexity in the convex setting, and empirically evaluate the approach against a competing method on several datasets.
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
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.
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.
arXiv:2610.28025v1 Announce Type: cross Abstract: This paper studies the problem of aggregation in multiclass classification, generalizing existing results for binary classification. Given a finite d...
arXiv:2607. 22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
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...
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.
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.
arXiv:2506. 10775v3 Announce Type: replace Abstract: In monotone classification, the input is a multi-set $P$ of points in $\mathbb{R}^d$, each associated with a hidden label from $\{-1, 1\}$.
arXiv:2607. 14889v1 Announce Type: new Abstract: This paper studies an optimal linear combination of binary classifiers based on a logical structuration of the dataset via truth tables.