82 citations · 149 across the 15 of their papers we have counts for
15 papers
Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
Jianqing Fan, Xin Tong, Yanhui Wu +2
Organizations often rely on statistical algorithms to make socially and economically impactful decisions. We must address the fairness issues in these important automated decisions…
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li +1
COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing…
Non-splitting Neyman-Pearson Classifiers
Jingming Wang, Lucy Xia, Zhigang Bao +1
The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., t…
Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm
Shunan Yao, Bradley Rava, Xin Tong +1
Label noise in data has long been an important problem in supervised learning applications as it affects the effectiveness of many widely used classification methods. Recently, imp…
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
Wei Vivian Li, Xin Tong, Jingyi Jessica Li
Asymmetric binary classification problems, in which the type I and II errors have unequal severity, are ubiquitous in real-world applications. To handle such asymmetry, researchers…
Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines
Jingyi Jessica Li, Xin Tong
Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypoth…