Label Error Detection
Label Error Detection is a methodological construct in machine learning that identifies incorrect annotations within labeled datasets by distinguishing anomalous label assignments from ground-truth-consistent labels based on model predictions, data patterns, or auxiliary validation criteria.
45
Publications
1.7K
Citations
199
Authors
79
Institutions
Publications per year
2017–2026
27
Parents
45
Eliminating class noise in large datasets
Xingquan Zhu, Xindong Wu, Qijun Chen · 2003 · 241 citations
Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment
Donghua Yu, Guojun Liu, Maozu Guo et al. · IEEE Access · 2019 · 98 citations · Full text
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Yifan Ding, Liqiang Wang, Deliang Fan et al. · 2018 · 98 citations · Full text
1–5 of 45
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Massachusetts Institute of Technology, United States
2 publications · 164 citations
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79

University of Trento
Trento, Italy
3 h-index
14 publications
554 citations
Jet Propulsion Laboratory
Pasadena, United States
2 h-index
6 publications
76 citations

University of Birmingham
Birmingham, United Kingdom
2 h-index
4 publications
66 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
2 h-index
5 publications
106 citations
Film Independent
Los Angeles, United States
2 h-index
2 publications
80 citations
Kyung Hee University
Seoul, South Korea
2 h-index
4 publications
173 citations
University of South Florida
Tampa, United States
2 h-index
4 publications
81 citations
Jilin University
Changchun, China
2 h-index
8 publications
230 citations
1–8 of 79
3 publications · 149 citations
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Repository · 2 publications · 113 citations
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2 publications · 107 citations
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2 publications · 42 citations
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1 publications · 12 citations
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