Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching (2410.15516v1)

Abstract: Privacy and regulatory constraints make data generation vital to advancing machine learning without relying on real-world datasets. A leading approach for tabular data generation is the Forest Flow (FF) method, which combines Flow Matching with XGBoost. Despite its good performance, FF is slow and makes errors when treating categorical variables as one-hot continuous features. It is also highly sensitive to small changes in the initial conditions of the ordinary differential equation (ODE). To overcome these limitations, we develop Heterogeneous Sequential Feature Forest Flow (HS3F). Our method generates data sequentially (feature-by-feature), reducing the dependency on noisy initial conditions through the additional information from previously generated features. Furthermore, it generates categorical variables using multinomial sampling (from an XGBoost classifier) instead of flow matching, improving generation speed. We also use a Runge-Kutta 4th order (Rg4) ODE solver for improved performance over the Euler solver used in FF. Our experiments with 25 datasets reveal that HS3F produces higher quality and more diverse synthetic data than FF, especially for categorical variables. It also generates data 21-27 times faster for datasets with $\geq20%$ categorical variables. HS3F further demonstrates enhanced robustness to affine transformation in flow ODE initial conditions compared to FF. This study not only validates the HS3F but also unveils promising new strategies to advance generative models.

Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching

This paper addresses the challenges of generating synthetic tabular data by introducing a novel method called Heterogeneous Sequential Feature Forest Flow (HS3F). The need for synthetic data arises due to privacy and regulatory constraints, driving the development of generative models that don't rely on real-world datasets. Traditional methods such as Forest Flow (FF) have shown promise but encounter limitations, particularly in handling categorical variables and sensitivity to ordinary differential equation (ODE) initial conditions. HS3F aims to overcome these issues by enhancing data generation efficiency and quality.

Methodology

HS3F builds upon the Forest Flow framework by introducing a sequential feature generation process. The paper details the weaknesses of FF, such as its slow performance and error propagation when dealing with one-hot encoded categorical variables. To address these, HS3F uses a feature-by-feature generation approach, which reduces reliance on noisy initial conditions by incorporating previously generated features, enhancing robustness and quality.

Continuous features in HS3F are generated using the Forest Flow approach, while categorical features are handled using multinomial sampling derived from an XGBoost classifier. This results in significantly faster data generation and better alignment with the real data distribution. Additionally, using a 4th order Runge-Kutta ODE solver provides enhanced performance compared to the Euler solver used in traditional FF methods.

Experimental Results

The experiments conducted across 25 datasets reveal that HS3F surpasses FF in producing more diverse and higher-quality synthetic data, particularly for datasets with a high percentage of categorical features. Impressively, HS3F achieves a 21-27 times speed improvement in data generation over FF with datasets containing at least 20% categorical variables. The method also demonstrates increased robustness to affine transformations in the ODE initial conditions, a known sensitivity in FF.

Results indicate that HS3F not only improves data generation speed but also maintains, and in some cases enhances, the quality of the data as measured by Wasserstein distances, F1 scores, R2 coefficients, and coverage metrics. Such metrics are crucial in assessing the fidelity of synthetic data to its real counterpart and its utility in downstream tasks.

Implications and Future Directions

This paper validates HS3F as a robust and efficient method for generating synthetic tabular data, which can advance the capabilities of machine learning models while respecting privacy constraints. The reduction in computational resources and time without sacrificing data quality marks a significant methodological improvement.

Future research could explore further optimization of the sequential feature generation process in HS3F, especially by identifying causal relationships in data to refine generation steps. Additionally, extending the model's applicability to different types of tabular data and integrating with other generative model frameworks could offer broader utility and integration in real-world applications.

The insights and methodological advancements presented in this paper provide a promising avenue for enhancing the landscape of synthetic data generation, addressing critical challenges present in contemporary data-driven research environments.