{
  "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 $ε_{N_1}=o_{N_1\\to\\infty}(1/N_1)$, 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_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09984",
  "author_total": 3,
  "authors": [
    {
      "name": "Samuel Gruffaz"
    },
    {
      "name": "Muhammad Fawad"
    },
    {
      "name": "Jaakko Nevalainen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/35xzxq3b615mp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09984",
  "fulltext_url": null,
  "id": "35xzxq3b615mp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09984",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Imbalanced Data Classification Techniques"
    ]
  ],
  "openalex": "W7220949666",
  "pdf_url": "https://arxiv.org/pdf/2610.09984",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/35xzxq3b615mp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:48Z",
      "native_id": "2610.09984",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09984"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative",
  "title_text": "Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:48Z",
  "url": "https://research.draconitic.com/paper/35xzxq3b615mp",
  "venue": "arXiv",
  "versions": []
}