# Neyman-Pearson classification algorithms and NP receiver operating characteristics

@article{Tong2016NeymanPearsonCA, title={Neyman-Pearson classification algorithms and NP receiver operating characteristics}, author={Xin Tong and Yang Feng and Jingyi Jessica Li}, journal={Science Advances}, year={2016}, volume={4} }

An umbrella algorithm and a graphical tool for asymmetric error control in binary classification. In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II…

## 59 Citations

### Non-splitting Neyman-Pearson Classifiers

- Computer Science
- 2021

Leveraging a canonical linear discriminant analysis model, a quantitative CLT is derived for a certain functional of quadratic forms of the inverse of sample and population covariance matrices and developed for the first time NP classifiers without splitting the training sample.

### Neyman-Pearson Classification Via Context Trees

- Computer Science2020 28th Signal Processing and Communications Applications Conference (SIU)
- 2020

An NP classification method that solves nonlinear problems via context trees in an online manner with an average of 66% increase in the area under the ROC curve along with a precise control over the desired type I error, compared to the algorithms that do not use context trees and can only solve linear problems.

### Asymmetric Error Control for Binary Classification in Medical Disease Diagnosis

- Computer Science, Medicine2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
- 2020

A tree-based classifier with asymmetric error control that predicts the risk of a ten-year cardiac disease, not only with improved accuracy and F1 score but also with full control over the number of false negatives.

### Active Learning for Online Nonlinear Neyman-Pearson Classification

- Computer Science2022 30th Signal Processing and Communications Applications Conference (SIU)
- 2022

An active learning method for online context tree based ensemble NP classifiers that prioritizes training samples that have high uncertainty (greater than a constant threshold) among different classifiers of the ensemble model is proposed.

### Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

- Computer ScienceArXiv
- 2021

This work studies the multiclass NP problem by connecting it to the CS problem and proposes two algorithms, believed to be the first work to solve the multi-class NP problem via cost-sensitive learning techniques with theoretical guarantees.

### Imbalanced classification: an objective-oriented review

- Computer Science
- 2020

An objective-oriented review of the common resampling techniques for binary classification under imbalanced class sizes is provided and the take-away message is that with imbalanced data, one usually should consider all the combinations of resamplings techniques and the base classification methods.

### Imbalanced classification: A paradigm‐based review

- Computer ScienceStat. Anal. Data Min.
- 2021

A paradigm‐based review of the common resampling techniques for binary classification under imbalanced class sizes, which considers the classical paradigm that minimizes the overall classification error, the cost‐sensitive learning paradigm, and the Neyman–Pearson paradigm.

### Instance-Based Classification Through Hypothesis Testing

- Computer ScienceIEEE Access
- 2021

The presented classification method can be regarded as an instance-based classifier based on hypothesis testing and is able to achieve the same level performance as several classic classifiers and has significantly better performance than existing testing- based classifiers.

### Neyman-Pearson Criterion (NPC): A Model Selection Criterion for Asymmetric Binary Classification

- Computer Science
- 2019

A real data case study of breast cancer suggests that the Neyman-Pearson criterion is a practical criterion that leads to the discovery of novel gene markers with both high sensitivity and specificity for breast cancer diagnosis.

### Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

- MedicineArXiv
- 2022

This work proposes a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-diagnosis errors with high probability on an integrated collection of single-cell RNA-seq datasets for 740 patients.

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