Tabular GANs for uneven distribution
This addresses data imbalance issues in tabular machine learning, but it is incremental as it applies existing GAN methods to a specific data type without introducing new techniques.
The paper tackles the problem of uneven data distribution between train and test sets in tabular data by using GANs to generate data that aligns the distributions, showing that this approach can improve model performance as an alternative option.
GANs are well known for success in the realistic image generation. However, they can be applied in tabular data generation as well. We will review and examine some recent papers about tabular GANs in action. We will generate data to make train distribution bring closer to the test. Then compare model performance trained on the initial train dataset, with trained on the train with GAN generated data, also we train the model by sampling train by adversarial training. We show that using GAN might be an option in case of uneven data distribution between train and test data.