1 citations · 2 across the 6 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2021★ 1 cited
A multi-schematic classifier-independent oversampling approach for imbalanced datasets
Saptarshi Bej, Kristian Schultz, Prashant Srivastava +2
Over 85 oversampling algorithms, mostly extensions of the SMOTE algorithm, have been built over the past two decades, to solve the problem of imbalanced datasets. However, it has b…
cs.LG2019
LoRAS: An oversampling approach for imbalanced datasets
Saptarshi Bej, Narek Davtyan, Markus Wolfien +2
The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class,…