Waldemar Hahn

Kristian Schultz, Saptarshi Bej, Waldemar Hahn et al.

Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classifier performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample generation, widely used for synthetic image generation. However, their scope on synthetic tabular data generation in the context of imbalanced classification is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation based approaches for imbalanced classification problems on smaller tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns the coefficients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classification on such small datasets, as compared to existing deep generative models, while being at-par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance and thus, improves the overall applicability of convex space learning.

Waldemar Hahn, Jan-Niklas Eckardt, Christoph Röllig et al.

The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring that synthetic datasets maintain high fidelity, utility, and adherence to domain-specific constraints remains a key challenge. While hyperparameter optimization (HPO) has been shown to improve generative model performance, the effectiveness of different optimization strategies for synthetic clinical data remains unclear. This study systematically evaluates four HPO strategies across eight generative models, comparing single-metric optimization against compound metric optimization approaches. Our results demonstrate that HPO consistently improves synthetic data quality, with TVAE, CTGAN, and CTAB-GAN+ achieving improvements of up to 60%, 39%, and 38%, respectively. Compound metric optimization outperformed single-metric strategies, producing more balanced and generalizable synthetic datasets. Interestingly, HPO alone is insufficient to ensure clinically valid synthetic data, as all models exhibited violations of fundamental survival constraints. Preprocessing and postprocessing played a crucial role in reducing these violations, as models lacking robust processing steps produced invalid data in up to 61% of cases. These findings underscore the necessity of integrating explicit domain knowledge alongside HPO to create high quality synthetic datasets. Our study provides actionable recommendations for improving synthetic data generation, with future research needed to refine metric selection and validate these findings on larger datasets to enhance clinical applicability.