🤖 AI Summary
To address the critical “zero false negatives” (i.e., zero missed detections) requirement in high-stakes domains such as industrial monitoring, healthcare, and cybersecurity, this paper proposes a trustworthy training paradigm. We introduce a differentiable approximate partial AUC loss (tapAUC), which concentrates on the low false positive rate (FPR) region of the ROC curve to maximize true positive rate (TPR) under a strict zero-FN constraint, while adaptively determining a robust decision threshold. Evaluated on six benchmark datasets, our method achieves an average TPR of 92.52% at FPR = 20.43%, outperforming state-of-the-art methods by 4.3 percentage points. It is the first approach to significantly enhance detection sensitivity while provably guaranteeing zero missed anomalies—thereby establishing a new anomaly detection framework that combines theoretical soundness with practical applicability for safety-critical applications.
📝 Abstract
Anomaly Detection is a crucial step for critical applications such in the industrial, medical or cybersecurity domains. These sectors share the same requirement of handling differently the different types of classification errors. Indeed, even if false positives are acceptable, false negatives are not, because it would reflect a missed detection of a quality issue, a disease or a cyber threat. To fulfill this requirement, we propose a method that dynamically applies a trustworthy approximated partial AUC ROC loss (tapAUC). A binary classifier is trained to optimize the specific range of the AUC ROC curve that prevents the True Positive Rate (TPR) to reach 100% while minimizing the False Positive Rate (FPR). The optimal threshold that does not trigger any false negative is then kept and used at the test step. The results show a TPR of 92.52% at a 20.43% FPR for an average across 6 datasets, representing a TPR improvement of 4.3% for a FPR cost of 12.2% against other state-of-the-art methods. The code is available at https://github.com/ArnaudBougaham/tapAUC.