Targeted synthetic data generation for tabular data via hardness characterization

• URL: http://arxiv.org/abs/2410.00759v1
• Date: Tue, 1 Oct 2024 14:54:26 GMT
• Title: Targeted synthetic data generation for tabular data via hardness characterization
• Authors: Tommaso Ferracci, Leonie Tabea Goldmann, Anton Hinel, Francesco Sanna Passino,
• Abstract summary: We introduce a novel augmentation pipeline that generates only high-value training points based on hardness characterization. We show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on simulated data and on a large scale credit default prediction task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic data generation has been proven successful in improving model performance and robustness in the context of scarce or low-quality data. Using the data valuation framework to statistically identify beneficial and detrimental observations, we introduce a novel augmentation pipeline that generates only high-value training points based on hardness characterization. We first demonstrate via benchmarks on real data that Shapley-based data valuation methods perform comparably with learning-based methods in hardness characterisation tasks, while offering significant theoretical and computational advantages. Then, we show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on simulated data and on a large scale credit default prediction task. In particular, our approach improves the quality of out-of-sample predictions and it is computationally more efficient compared to non-targeted methods.

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