# Neyman-Pearson Classification under High-Dimensional Settings

arXiv: https://arxiv.org/abs/1508.03106 · HTML: https://mlsanity.com/paper/1508.03106/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2015.

## Built on by (most cited first, 5 of 5)

- [Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers](https://mlsanity.com/paper/2208.11111/) (2022)
- [Neyman-Pearson Multi-class Classification via Cost-sensitive Learning](https://mlsanity.com/paper/2111.04597/) (2021)
- [Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm](https://mlsanity.com/paper/2112.00314/) (2021)
- [Distribution-Free Rates in Neyman-Pearson Classification](https://mlsanity.com/paper/2402.09560/) (2024)
- [Classification with Abstention Under Class-Conditional Error Constraints](https://mlsanity.com/paper/2609.22632/) (2026)

## Abstract (arXiv)

Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical performance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classification. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies.