Publikation
Intelligent Data Engineering and Automated Learning – IDEAL 2025
Luis Martínez; David Camacho; Hujun Yin; Bapi Dutta; Raciel Yera; Rosa María Rodríguez Domínguez; Antonio Tallón-Ballesteros (Hrsg.)
International Conference on Intelligent Data Engineering and Automated Learning (IDEAL-2025), 26th International Conference on Intelligent Data Engineering and Automated Learning, November 13-15, Jaén, Spain, LNCS, Vol. 16238, ISBN 978-3-032-10485-4, Springer, Cham, 11/2025.
Zusammenfassung
Modern deep learning systems are constrained by a persistent shortage of sufficiently large, high-quality training data. Synthesis of
tabular training data using generative models such as conditional GANs
offers a promising solution, but the utility of current models is still limited
as they show problems in capturing complex relationships of categorical
features. Therefore, we introduce the concept of multi-dependence conditional vectors (M-DCV ), which allow for setting multiple conditions
on the creation of categorical data directly during the training and sampling process of conditional GANs. We propose three different strategies
to create M-DCV : first, based on Bayesian Networks (M-DCV Bayes),
second on the association measurement Cramer’s V (M-DCV Cramer),
and third one involving a cloning process (M-DCV RPC). We evaluate
on two tabular GANs and three datasets using a unified pipeline for
utility, fidelity, and privacy. Across all experiments, there is an overall
average improvement in the quality of categorical fidelity by 33.2% with
the RPC approach, 13.2% with the Bayes method, and 8.8% using the
Cramer modification. Additionally, we can further enhance the machine
learning utility with all M-DCV variants, although this comes at the
cost of reduction in privacy scores