1 citations · 1 across the 2 of their papers we have counts for
3 papers
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets
Kristian Schultz, Saptarshi Bej, Waldemar Hahn +3
Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to…
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…
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,…