Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification

DOI10.1080/01621459.2020.1740711zbMath1457.62203arXiv1802.02558OpenAlexW3011695483MaRDI QIDQ5857126

Lucy Xia, Richard Zhao, Yanhui Wu, Xin Tong

Publication date: 30 March 2021

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

Full work available at URL: https://arxiv.org/abs/1802.02558

zbMATH Keywords

censorshiptext classificationtype I errorsocial mediadata distortionNeyman-Pearson classification paradigm

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computing methodologies for text processing; mathematical typography (68U15)

Related Items (2)

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data ⋮ Optimal subgroup selection

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• Neyman-Pearson classification, convexity and stochastic constraints

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