Synthetic Generation of Multidimensional Data to Improve Classification Model Validity

This paper aims to compare Generative Adversarial Network (GAN) models and feature selection methods for generating synthetic data in order improve the validity of a classification model. The generation technique involves new samples from existing increase diversity help model generalize better. multidimensional aspect refers fact that it can have multiple features or variables describe it. GAN proven be effective preserving statistical properties original data. However, augmentation is crucial build robust accurate predictive models. By comparing different with on multi-dimensional datasets, this determine best combination support Data