Targeted synthetic data generation for tabular data via hardness characterization

• URL: http://arxiv.org/abs/2410.00759v2
• Date: Sat, 08 Feb 2025 13:33:51 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 simple augmentation pipeline that generates only high-value training points based on hardness characterization. Our approach improves the quality of out-of-sample predictions and it is computationally more efficient compared to non-targeted methods.
• Score: 0.0
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• Abstract: Data augmentation via synthetic data generation has been shown to be effective 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 simple augmentation pipeline that generates only high-value training points based on hardness characterization, in a computationally efficient manner. We first empirically demonstrate via benchmarks on real data that Shapley-based data valuation methods perform comparably with learning-based methods in hardness characterization tasks, while offering significant computational advantages. Then, we show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on a number of tabular datasets. 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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