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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18741 |
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| _version_ | 1866916590901002240 |
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| author | Liu, Dan Kababji, Samer El Mitsakakis, Nicholas Pilgram, Lisa Walters, Thomas Clemons, Mark Pond, Greg El-Hussuna, Alaa Emam, Khaled El |
| author_facet | Liu, Dan Kababji, Samer El Mitsakakis, Nicholas Pilgram, Lisa Walters, Thomas Clemons, Mark Pond, Greg El-Hussuna, Alaa Emam, Khaled El |
| contents | Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data. We found that augmentation improves prognostic performance for datasets that: have fewer observations, with smaller baseline AUC, have higher cardinality categorical variables, and have more balanced outcome variables. No specific generative model consistently outperformed the others. We developed a decision support model that can be used to inform analysts if augmentation would be useful. For seven small application datasets, augmenting the existing data results in an increase in AUC between 4.31% (AUC from 0.71 to 0.75) and 43.23% (AUC from 0.51 to 0.73), with an average 15.55% relative improvement, demonstrating the nontrivial impact of augmentation on small datasets (p=0.0078). Augmentation AUC was higher than resampling only AUC (p=0.016). The diversity of augmented datasets was higher than the diversity of resampled datasets (p=0.046). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthetic Data Generation for Augmenting Small Samples Liu, Dan Kababji, Samer El Mitsakakis, Nicholas Pilgram, Lisa Walters, Thomas Clemons, Mark Pond, Greg El-Hussuna, Alaa Emam, Khaled El Machine Learning Artificial Intelligence Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data. We found that augmentation improves prognostic performance for datasets that: have fewer observations, with smaller baseline AUC, have higher cardinality categorical variables, and have more balanced outcome variables. No specific generative model consistently outperformed the others. We developed a decision support model that can be used to inform analysts if augmentation would be useful. For seven small application datasets, augmenting the existing data results in an increase in AUC between 4.31% (AUC from 0.71 to 0.75) and 43.23% (AUC from 0.51 to 0.73), with an average 15.55% relative improvement, demonstrating the nontrivial impact of augmentation on small datasets (p=0.0078). Augmentation AUC was higher than resampling only AUC (p=0.016). The diversity of augmented datasets was higher than the diversity of resampled datasets (p=0.046). |
| title | Synthetic Data Generation for Augmenting Small Samples |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.18741 |