arXiv (All)
open access
Partial AUC Maximization from Positive-unlabeled Data
The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes true positive rates within a specific range of false positive rates (FPRs). Classifiers that achieve high pAUC need to be obtained in many real-world
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Classification with Abstention Under Class-Conditional Error Constraints#1037315
- Reducing the overfitting in the gROC curve estimation#936037
- Federated Computation of ROC and PR Curves#1118425
- On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves#141722
- Practical estimation of the optimal classification error with soft labels and calibration#147095
Record · ID 1154907
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive