Feature Transformation

Feature transformation aims to improve the performance of machine learning models by mathematically altering input features into a more effective representation space. Current research focuses on automating this process, often employing reinforcement learning, evolutionary algorithms, or generative models to discover optimal transformations, sometimes within a graph-based framework. These advancements are significant because they address limitations of manual feature engineering, improving model accuracy, generalization, and interpretability across diverse applications, including image processing, federated learning, and material science.