HNPclassifier: An R Package for Hierarchical Neyman-Pearson Classification

2026-06-11

AI Summary

This work addresses under-classification errors in ordinal multi-class classification—specifically, the misclassification of high-priority instances as lower-priority classes—by proposing a hierarchical Neyman-Pearson (H-NP) classification framework that rigorously controls such error rates. The method introduces the first flexible H-NP classifier capable of integrating diverse built-in scoring functions (e.g., logistic regression, random forests, support vector machines) or user-defined ones, explicitly designed for ordered multi-class settings. Empirical evaluations demonstrate that the proposed approach satisfies user-specified under-classification error constraints with high probability, offering both practical utility and strong scalability while maintaining strict statistical guarantees on error control.

Abstract

In multi-class classification problems, classes often have a natural priority ordering (e.g., cancer stages, COVID-19 severity levels, or air-quality categories). In such settings, it is important to prioritize correct identification of more severe classes and to control under-classification errors, which occur when an observation from a higher-priority class is misclassified into a lower-priority one. The Hierarchical Neyman-Pearson (H-NP) framework of Wang et al. (2024) was developed for ordered multi-class settings to prioritize under-classification error control; its H-NP umbrella algorithm provides high-probability control of under-classification errors at user-specified levels. This paper introduces the R package HNPclassifier, which implements H-NP umbrella algorithms to construct H-NP classifiers using built-in learners such as logistic regression, random forests, and support vector machines, as well as user-supplied scoring functions, thereby enabling effective error control for ordered multi-class classification tasks.

Problem

• hierarchical classification
• Neyman-Pearson classification
• under-classification error
• ordered multi-class
• error control

Innovation

• Hierarchical Neyman-Pearson
• under-classification error control
• ordered multi-class classification
• H-NP umbrella algorithm
• R package