zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation

TL;DR

zGAN is a novel generative adversarial network designed to produce realistic synthetic tabular data with outlier characteristics, improving model robustness and outlier detection in various applications.

Contribution

The paper introduces zGAN, a new GAN architecture capable of generating correlated outliers in synthetic data, enhancing data augmentation for outlier-sensitive tasks.

Findings

zGAN effectively replicates feature correlations in synthetic data.

It successfully generates outliers based on real data covariances.

Experiments show improved model performance with zGAN-augmented data.

Abstract

The phenomenon of "black swans" has posed a fundamental challenge to performance of classical machine learning models. The perceived rise in frequency of outlier conditions, especially in post-pandemic environment, has necessitated exploration of synthetic data as a complement to real data in model training. This article provides a general overview and experimental investigation of the zGAN model architecture developed for the purpose of generating synthetic tabular data with outlier characteristics. The model is put to test in binary classification environments and shows promising results on realistic synthetic data generation, as well as uplift capabilities vis-\`a-vis model performance. A distinctive feature of zGAN is its enhanced correlation capability between features in the generated data, replicating correlations of features in real training data. Furthermore, crucial is the ability of zGAN to generate outliers based on covariance of real data or synthetically generated covariances. This approach to outlier generation enables modeling of complex economic events and augmentation of outliers for tasks such as training predictive models and detecting, processing or removing outliers. Experiments and comparative analyses as part of this study were conducted on both private (credit risk in financial services) and public datasets.