Training Population Augmentation
Training Population Augmentation is a methodological contribution in the field of machine learning that expands the composition of the training population through the systematic incorporation of additional data instances, synthetic samples, or diverse representations to enhance model generalizability and robustness.
31
Publications
2.5K
Citations
115
Authors
51
Institutions
Publications per year
2017–2026
28
Parents
31
Data augmentation: A comprehensive survey of modern approaches
Alhassan Mumuni, Fuseini Mumuni · Array · 2022 · 800 citations · Full text
KNNOR: An oversampling technique for imbalanced datasets
Ashhadul Islam, Samir Brahim Belhaouari, Atiq Ur Rehman et al. · Applied Soft Computing · 2021 · 132 citations · Full text
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University of California, Berkeley
Berkeley, United States
2 h-index
11 publications
808 citations

Universidade Federal do Ceará
Fortaleza, Brazil
2 h-index
2 publications
71 citations

University of Edinburgh
Edinburgh, United Kingdom
1 h-index
2 publications
158 citations

International Rice Research Institute
Los Baños, Philippines
1 h-index
2 publications
68 citations
University of Portsmouth
Portsmouth, United Kingdom
1 h-index
1 publications
40 citations

Henan University
Kaifeng, China
1 h-index
1 publications
78 citations

University of Liverpool
Liverpool, United Kingdom
1 h-index
2 publications
86 citations

University of Mines and Technology
Tarkwa, Ghana
1 h-index
1 publications
800 citations
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Repository · 4 publications · 205 citations
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2 publications · 103 citations
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1 publications · 12 citations
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1 publications · 29 citations
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1 publications · 34 citations
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