Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative
stat.ML, cs.LG, stat.AP, stat.ME
Submitted: 2026-10-07
Updated: 2026-10-07
License: http://creativecommons.org/licenses/by/4.0/
The gist: While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier with an almost zero false negative rate remains
Terminology
Abstract
While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier with an almost zero false negative rate remains largely unexplored. In this paper, we introduce the Extreme Binary Classification problem, where the objective is to learn a classifier whose false negative rate α is constrained by ε N 1=o N 1 to infinity(1/N 1), with N 1 denoting the number of positive examples in the training set. To address this problem, we propose a threshold adaptation method theoretically grounded in guarantees derived from Extreme Value Theory, together with a feature selection procedure based on a permutation test applied to sample maxima. Experimental results on four real-world datasets of varying sizes demonstrate that our approach compares favorably with state-of-the-art methods. In addition, we illustrate its interpretability through an application to a cancer screening dataset.
Sources
- A general estimator for the right endpoint - with an application to supercentenarian women's records
- MAPIE: an open-source library for distribution-free uncertainty quantification
Related papers
- Behavior of prediction performance metrics with rare events
- Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
- A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
- Scalable Posterior Uncertainty for Flexible Density-Based Clustering
- Efficient quadratic entropy with distance sketches
- Embedding-Bias in Conditional Independence Testing