Asymmetric Error Control Under Imperfect Supervision: A Label-Noise-Adjusted Neyman–Pearson Umbrella Algorithm-Reference-Cited by-同舟云学术

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Asymmetric Error Control Under Imperfect Supervision: A Label-Noise-Adjusted Neyman–Pearson Umbrella Algorithm

Published:2022-01-28 Issue: Volume: Page:1-13
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Yao Shunan¹,Rava Bradley²,Tong Xin²,James Gareth²

Affiliation:

1. Department of Mathematics, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA

2. Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA

Funder

NSF

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2021.2016423

Reference31 articles.

1. Classification with asymmetric label noise: Consistency and maximal denoising

2. Machine Learning, Meta-Reasoning and Logics

3. Identifying Mislabeled Training Data

4. Identifying Mislabeled Training Data

5. Classification with imperfect training labels

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1. Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard;Biometrics;2024-07-01

2. Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data;Journal of the American Statistical Association;2023-10-17

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