Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative (arxiv.org)
arXiv:2610.09984v1 Announce Type: new
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 $\alpha$ is constrained by $\epsilon_{N_1}=o_{N_1\to\infty}(1/N_1)$, 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.
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 $\alpha$ is constrained by $\epsilon_{N_1}=o_{N_1\to\infty}(1/N_1)$, 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.
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