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Data Augmentation Using GANs
Fabio Henrique Kiyoiti dos Santos Tanaka, Claus Aranha
TL;DR
The paper addresses whether GANs can generate useful artificial training data for imbalanced classification and for reducing reliance on sensitive original datasets. It evaluates GAN-generated numerical data with Decision Trees across fully synthetic training and minority-class oversampling settings. Fully synthetic GAN data was suitable for the proposed tasks and sometimes matched or exceeded original-data performance, while GAN oversampling improved over imbalanced data but did not outperform SMOTE or ADASYN.
Problem
Imbalanced datasets can hinder minority-class classification, while sensitive original datasets may be risky to use directly.
Method
The study uses GANs to generate synthetic numerical datasets, then evaluates Decision Tree classifiers trained on fully synthetic data or GAN-augmented minority classes.
Results
GAN-generated data was suitable for the proposed tasks; fully synthetic training sometimes exceeded original-data performance, while GAN oversampling beat imbalanced data but not SMOTE or ADASYN.
Takeaways & Limitations
GAN-generated numerical data can support classifier training without directly using the original dataset and can improve performance over an imbalanced baseline.
Takeaways & Limitations
The study covers one GAN architecture, six network variations, and three classification benchmark datasets.
Abstract
from arXiv · showhide
In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information, and it is desirable to avoid using the original data set as much as possible (example: medical data). We test our proposal on benchmark data sets using different network architectures, and show that a Decision Tree (DT) classifier trained using the training data generated by the GAN reached the same, (and surprisingly sometimes better), accuracy and recall than a DT trained on the original data set.
1. Introduction
Synthetic data can address imbalanced classification and privacy concerns by expanding minority classes or replacing sensitive original datasets. The paper evaluates GAN-generated numerical data as classifier training data and compares it with original data and established oversampling methods.
- High-quality synthetic data can improve coverage of real-world cases and support generalization while enabling minority-class oversampling and privacy-preserving dataset generation.
- Extreme class imbalance, such as 10000 to 1 in fraud data, can make classification algorithms struggle to identify underrepresented classes.
- Sensitive attributes in medical records can permit re-identification even without names, motivating synthetic alternatives to direct use of original data.
- GAN-generated synthetic training data was evaluated against original training data using Decision Tree Accuracy, Precision, and Recall, and against GAN, SMOTE, and ADASYN oversampling on imbalanced datasets.In some cases synthetic-data classifiers performed better than original-data classifiers; GAN augmentation improved over the imbalanced baseline but not over SMOTE or ADASYN.
2. Background
GANs generate data through competition between a generator and discriminator, while SMOTE and ADASYN create minority-class samples using neighboring data points. The paper situates its numerical-data focus within broader GAN applications and notes practical training difficulties.
- Generative Adversarial Networks (GANs): GANs are generative models composed of a generator that creates data instances and a discriminator that evaluates whether samples are authentic.
- Generative Adversarial Networks (GANs): The discriminator learns from real and generated samples, while the generator maps latent noise to samples resembling the original data distribution.
- Generative Adversarial Networks (GANs): Training both networks simultaneously makes the discriminator better at distinguishing real from fake data and the generator progressively closer to the original data.
- Generative Adversarial Networks (GANs): GANs can generate high-quality data but are difficult to train, computationally demanding, and potentially unstable or prone to overfitting.
- Generative Adversarial Networks (GANs): Earlier GAN applications for balancing data and generating synthetic datasets primarily focused on images, whereas this work explores standard numerical databases.
- SMOTE and ADASYN: SMOTE interpolates between minority-class points and nearest neighbors, while ADASYN adds a small random bias to increase synthetic-data variance.
3. Proposal and Experimental Design
The study tests GAN-generated numerical data in two settings: fully synthetic classifier training and minority-class oversampling. It uses a simple GAN architecture, Decision Tree evaluation, and three benchmark datasets.
- The experiments evaluate GANs for training classifiers entirely on synthetic numerical data and for balancing imbalanced datasets through minority-class oversampling.
- For fully synthetic training, classifier performance on GAN-generated data is compared with performance on the original dataset, while synthetic data can reduce distribution of the original data.
- For imbalance experiments, GAN, SMOTE, and ADASYN oversampling are compared with non-oversampled data, including attention to whether GANs address sparse data and outliers.
- The study uses one general numerical-data GAN architecture with six layer-and-node configurations, standard neural-network components, and no convolutional layers.The design intentionally remains simple to focus on generating synthetic numerical databases.
- Decision Trees serve as evaluation classifiers because they are interpretable and require little to no data preparation.
- The evaluation uses three benchmark datasets: diabetes, breast cancer, and highly imbalanced credit-card fraud data.The datasets contain numeric attributes suitable for the selected GAN setting, and attributes are scaled to [0,1] before use.
4. Results and Analysis
Across fully synthetic-data and minority-class oversampling experiments, GAN-generated data broadly reproduced or improved classifier performance, while trade-offs varied by dataset and metric.
- Experiment design: The experiments trained GANs on original data, generated equal-sized synthetic sets, converted continuous labels by rounding, and trained Decision Trees on the generated data.The trees were tested on the original test subsets; GANs were also trained with labels included as attributes.
- Fully synthetic data: Early cancer and diabetes results showed similar class and attribute distributions without explicitly separating classes before GAN training.This reduced preparation burden for data providers and domain-knowledge requirements for data users.
- Fully synthetic data: The 256/512-layer architecture had the best overall mean accuracy, precision, and recall in both datasets, with a statistically significant accuracy difference (p < 0.05).The authors used this architecture as the baseline for subsequent comparisons.
- Fully synthetic data: Synthetic-data accuracy was close to the original-data accuracy for diabetes and better than the original-data accuracy for cancer.This comparison was presented as evidence that GAN-generated training data is promising for classifier training.
- Fully synthetic data: GAN-generated recall was strong for diabetes but slightly lower than original-data recall for cancer, while privacy evaluation used Euclidean distance between synthetic and original points.The reported large distances, especially for cancer, suggested that exact user information was unlikely to be pinpointed from the synthetic data.
- Minority-class oversampling: For minority-class oversampling, all methods improved recall and worsened precision; on imbalanced tests, GAN had better accuracy and precision but worse recall than ADASYN and SMOTE.The authors therefore preferred ADASYN and SMOTE for credit-card fraud, where identifying fraud was considered more important.
- Minority-class oversampling: GAN oversampling still improved results over using the original dataset alone, although the authors identified catastrophic training divergence and metric trade-offs as concerns.They suggested that further architectural and parameter work might produce results comparable to ADASYN and SMOTE.
5. Conclusions and Future Work
GAN-generated synthetic data was suitable for training classifiers without the original dataset and for oversampling minority classes. Results were stronger than the original data in balanced settings, but remained below SMOTE or ADASYN in imbalanced settings, while the study’s scope was limited.
- GAN-generated datasets were suitable for training classifiers without using the original data and for oversampling minority classes.
- Balanced-scenario training with only GAN synthetic data achieved better accuracy and precision than training on the original dataset.
- In imbalanced scenarios, GAN synthetic data performed better than the original data but not better than SMOTE or ADASYN.
- GANs can reduce the need for users to define rules or constraints when generating synthetic data.
- The initial inquiry used one GAN architecture, six depth-and-width variations, and three classification benchmark datasets.