Khái niệm cốt lõi
A robust statistical framework for evaluating and ranking the performance of synthetic data generation models based on their ability to produce high-quality synthetic data.
Tóm tắt
The paper presents a new evaluation framework for assessing the quality of synthetic data generated by various models. The key highlights are:
The framework employs a suite of multivariate evaluation tests, including Wasserstein-Cramer's V, Novelty, Domain Classifier, and Anomaly Detection, to comprehensively measure the quality of the generated synthetic data.
The framework utilizes statistical analysis techniques, specifically the Friedman Aligned-Ranks (FAR) test and Finner post-hoc test, to rank the synthetic data generation models and determine if there are significant differences in their performance.
The proposed approach provides strong theoretical and statistical evidence about the models' ranking and the overall evaluation process. It is flexible and adaptive, allowing for the integration of new evaluation tests as needed.
The framework was applied to two real-world datasets, demonstrating its ability to evaluate the quality of synthetic data generated by state-of-the-art models, such as Gaussian Copula, Gaussian Mixture Models (GMM), Conditional Tabular Generative Adversarial Network (CTGAN), Table Variational Auto-Encoder (TVAE), and Copula Generative Adversarial Network (CopulaGAN).
The results highlight the difficulty in identifying the best synthetic data generation model based on individual evaluation tests, emphasizing the need for a comprehensive statistical framework like the one proposed in this work.
Thống kê
"The Friedman statistic FAR with 4 degrees of freedom is equal to 5.675, while the p-value is equal to 0.22, which suggests that the post-hoc test should be applied in order to examine the existence of significant differences among the models' performance."
"Friedman statistic FAR with 3 degrees of freedom is equal to 3.339, while the p-value is equal to 0.34. This suggests that Finner post-hoc test should be applied in order to examine the existence of statistical significant differences relative to the evaluated models' ability to generate quality data."
Trích dẫn
"The proposed approach is able to provide strong theoretical and statistical evidence about the models' ranking and the overall evaluation process."
"The use case scenarios on two real-world datasets demonstrated the applicability of the proposed framework and its ability for evaluating state-of-the-art synthetic data generation models."