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

DOI10.1080/01621459.2023.2270657arXiv2210.02197OpenAlexW4387694179MaRDI QIDQ6153964

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Publication date: 19 March 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2210.02197

zbMATH Keywords

multi-class classificationasymmetric error controlscRNA-seq data featurization

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Classification with confidence
• A Neyman–Pearson Approach to Statistical Learning
• The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases
• Neyman-Pearson classification, convexity and stochastic constraints
• Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification
• Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data
• Asymmetric Error Control Under Imperfect Supervision: A Label-Noise-Adjusted Neyman–Pearson Umbrella Algorithm
• Is a Classification Procedure Good Enough?—A Goodness-of-Fit Assessment Tool for Classification Learning

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