arXiv AI
Partial AUC Maximization from Positive-unlabeled Data
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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.
arXiv Machine Learning
Sep 22
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...
arXiv Statistics ML
Sep 23
Context-Adaptive Thresholding for Conditionally Representative Monitoring and Classification
arXiv:2609.26652v1 Announce Type: cross Abstract: Commonly, classifiers and monitoring procedures are trained from labeled data by optimizing an objective such as the misclassification rate. This may...
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.
arXiv Machine Learning
Jul 16
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
arXiv:2607. 13428v1 Announce Type: new Abstract: Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones.
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.