Label Error Prevention
Label Error Prevention is a methodological framework within the domain of data quality management that specifically identifies, mitigates, and prevents inaccuracies in labeled datasets through systematic validation, correction, and monitoring processes to ensure the reliability of supervised learning models.
10
Publications
407
Citations
51
Authors
20
Institutions
Publications per year
2017–2026
10
Parents
10
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
CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning
Zeren Sun, Xian‐Sheng Hua, Yazhou Yao et al. · 2020 · 56 citations
Self-Error-Correcting Convolutional Neural Network for Learning with Noisy Labels
Xin Liu, Shaoxin Li, Meina Kan et al. · 2017 · 32 citations
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Nanjing University of Science and Technology
Nanjing, China
2 h-index
4 publications
193 citations

Harvard University Press
Cambridge, United States
1 h-index
1 publications
11 citations
Tencent (China)
Shenzhen, China
1 h-index
1 publications
32 citations
University of Technology Sydney
Sydney, Australia
1 h-index
1 publications
56 citations

Harbin Institute of Technology
Harbin, China
1 h-index
3 publications
294 citations
Jilin University
Changchun, China
1 h-index
1 publications
41 citations
Menlo School
Atherton, United States
1 h-index
8 publications
88 citations
University of Chinese Academy of Sciences
Beijing, China
1 h-index
4 publications
128 citations
1–8 of 20
IMWUT · 1 publications · 41 citations
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1 publications · 25 citations
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1 publications · 98 citations
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Conference · 1 publications · 16 citations
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