This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.
Learning from Imbalanced Data in Presence of Noisy and Borderline Examples
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.