Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Publication Details

AICS 2020: 28th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin Ireland,

Abstract

Deep learning based methods based on Generative Adversarial Networks (GANs) have seen remarkable success in data synthesis of images and text. This study investigates the use of GANs for the generation of tabular mixed dataset. We apply Wasserstein Conditional Generative Adversarial Network (WCGAN-GP) to the task of generating tabular synthetic data that is indistinguishable from the real data, without incurring information leakage. The performance of WCGAN-GP is compared against both the ground truth datasets and SMOTE using three labelled real-world datasets from different domains. Our results for WCGAN-GP show that the synthetic data preserves distributions and relationships of the real data, outperforming the SMOTE approach on both class preservation and data protection metrics. Our work is a contribution towards the automated synthesis of tabular mixed data

DOI

https://doi.org/10.21427/e6wa-sz92

Download


Share

COinS