Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study

TL;DR

This paper compares different synthetic tabular data generation methods and sampling strategies to address class and group imbalances, aiming to improve fairness and utility in machine learning models.

Contribution

It provides a comprehensive comparison of state-of-the-art generative models and sampling techniques for mitigating class and group imbalances in tabular data.

Findings

Generative models effectively reduce bias in imbalanced datasets

Sampling strategies improve fairness without sacrificing accuracy

Experimental results on four datasets validate the approaches

Abstract

Due to their data-driven nature, Machine Learning (ML) models are susceptible to bias inherited from data, especially in classification problems where class and group imbalances are prevalent. Class imbalance (in the classification target) and group imbalance (in protected attributes like sex or race) can undermine both ML utility and fairness. Although class and group imbalances commonly coincide in real-world tabular datasets, limited methods address this scenario. While most methods use oversampling techniques, like interpolation, to mitigate imbalances, recent advancements in synthetic tabular data generation offer promise but have not been adequately explored for this purpose. To this end, 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. Experimental results on four datasets, demonstrate the effectiveness of generative models for bias mitigation, creating opportunities for further exploration in this direction.