Data Augmentation Using GANs (1904.09135v1)

Abstract: 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.

• The paper shows that GAN-generated data can match classifier performance on key metrics compared to original datasets.
• Experiments reveal that specific GAN architectures can improve accuracy and precision over conventional methods like SMOTE and ADASYN.
• The study highlights the potential of GANs to address data imbalances and privacy concerns in machine learning applications.

Analyzing the Role of GANs in Data Augmentation and Classifier Training

This paper by Tanaka and Aranha investigates the use of Generative Adversarial Networks (GANs) for data augmentation within machine learning tasks, specifically focusing on two key areas: creating synthetic data for classification tasks and addressing data imbalances by oversampling minority classes. Their research provides quantitative insights into the efficacy of GANs compared to traditional methods like SMOTE and ADASYN for generating synthetic data in numerical datasets.

The paper evaluates GANs' potential through two primary domains: improving classifier performance by training solely on GAN-generated datasets, and enhancing the efficacy of classification models in imbalanced datasets through synthetic oversampling. The GAN architectures considered in the experiments vary mainly in terms of network depth and the size of hidden layers. Decision Tree classifiers are employed to benchmark the results, owing to their interpretability and minimal preprocessing requirements.

Synthetic Data Generation and Classifier Training

An intriguing aspect of this research centers on enabling classifiers to achieve comparable or even improved performance using GAN-generated data instead of the original dataset. Experimentation on numerical datasets such as the Pima Indians Diabetes Database and the Breast Cancer Wisconsin Dataset reveals nuanced findings. The synthetic data, in certain GAN configurations, enabled classifiers to perform with precision and accuracy measures aligning closely with those achieved using original datasets. Specifically, the 256/512 layer architecture demonstrated promising results in both accuracy and precision across datasets, highlighting the GANs' capability to alleviate overfitting issues inherent to the original data.

Addressing Data Imbalance

The paper also explores GAN-generated data in contexts of highly imbalanced datasets, such as credit card fraud detection. GANs were compared against SMOTE and ADASYN concerning their ability to rebalance classes by generating synthetic samples for minority classes. Results demonstrate that while GANs enhance classification performance over imbalanced datasets, they did not consistently outperform ADASYN and SMOTE. The emphasis here is on how GAN variations still raised both accuracy and precision but did not surpass the benchmarks set by the other two resampling techniques in recall, which is a crucial metric for imbalanced scenarios like fraud detection.

Theoretical Implications and Future Directions

The implications of this paper have pertinent applications in fields that require privacy-preserving techniques or suffer from data scarcity in minority class instances. Synthetic datasets being similar but not identical to the original could lead to avoiding sensitive data exposure, thereby effectively addressing privacy concerns in machine learning model training.

Future research directions should consider experimenting with a broader diversity of GAN architectures and optimizers, as well as exploring autoencoder-assisted GAN setups to enhance data generation quality. Expanding validations across more varied datasets with different structural characteristics and challenges will be beneficial. Moreover, the integration of feature diversity, the impact of mislabeled data, and optimal GAN configurations tailored for specific data typologies should be continual areas of focus for forthcoming studies.

In summary, this paper substantiates the versatility of GANs for data augmentation in machine learning, presenting them as a feasible alternative to conventional resampling techniques under certain conditions. Their ability to generate synthetic yet useful data for classifier training remains a pivotal consideration for practitioners aiming to leverage machine learning in domains with stringent data privacy requirements or class imbalance issues.