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Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative
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The University of Queensland, Australia
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Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative

Samuel Gruffaz, Muhammad Fawad, Jaakko Nevalainen

Tampere university, Finland

Binary Classification

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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\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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