# Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative

arXiv: https://arxiv.org/abs/2610.09984 · HTML: https://mlsanity.com/paper/2610.09984/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 1 of 1)

- [MAPIE: an open-source library for distribution-free uncertainty quantification](https://mlsanity.com/paper/2207.12274/) (2022)

## Abstract (arXiv)

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 εN1=oN1→∞(1/N1), with N1 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.