FCT-GAN: Fourier Neural Operator for Global Relation Enhancement in Tabular Data Synthesizing using Generative Adversarial Networks

Abstract

Since the regularization of data privacy (e.g.,
GDPR), the effectiveness of data sharing has decreased. A promising technique to circumvent this
problem is tabular data synthesis (i.e., the generation of fake tabular data that statistically resembles the original data). However, the state-of-the-art tabular data synthesis model, CTAB-GAN,
fails at robustly imitating global data dependencies
and underperforms when column orders get permuted. CTAB-GAN internally uses Convolutional
Neural Networks (CNN) which limits the model’s
performance due to a strictly non-global data perspective during iterative training phases. To address this limitation, this paper proposes FCT-GAN which leverages the Fourier Neural Operator to
learn global dependencies in the frequency domain.
Specifically, it enhances CTAB-GAN by replacing
the CNN of the discriminator with a four-layered
two-dimensional Fourier Neural Operator. As a
consequence of FCT-GAN’s global nature and cross-column relation robustness, it outperforms CTAB-GAN and additionally offers the column permutation invariant property. The evaluation of FCT-GAN
on five datasets shows that the generated data, remarkably resembles the real data and reveals an increase in accuracy, by up to 19% for five machine
learning algorithms independent of data column order, compared to CTAB-GAN.