Official code for our paper titled:
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
accepted at the 4th Workshop on Bias and Fairness in AI, ECML PKDD 2024, 13th of September, Vilnius (Lithuania)
This paper conducts a comparative analysis to address class and group imbalances using state-of-the-art models for synthetic tabular data generation and various sampling strategies.
Figure 1: Distributions of class and group imbalance for each real dataset (first column) along with final augmented dataset for each sampling strategy.