82 citations · 149 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
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…
Imbalanced classification: a paradigm-based review
Yang Feng, Min Zhou, Xin Tong
A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data…
A flexible model-free prediction-based framework for feature ranking
Jingyi Jessica Li, Yiling Chen, Xin Tong
Despite the availability of numerous statistical and machine learning tools for joint feature modeling, many scientists investigate features marginally, i.e., one feature at a time…
Generalized Pearson correlation squares for capturing mixtures of bivariate linear dependences
Jingyi Jessica Li, Xin Tong, Peter J. Bickel
Motivated by the pressing needs for capturing complex but interpretable variable relationships in scientific research, here we generalize the squared Pearson correlation to capture…
Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification
Lucy Xia, Richard Zhao, Yanhui Wu +1
This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimi…