Composite Variable Construction

Composite variable construction focuses on creating new, informative variables from existing data to improve the performance of machine learning models, particularly in regression and classification tasks. Current research explores diverse approaches, including using classifiers to discretize continuous variables and enhance feature representation, employing neural networks for dimensionality reduction and feature extraction, and adapting models to handle variable numbers of agents in multi-agent systems. These advancements are significant for improving model accuracy, interpretability, and efficiency across various domains, from robotics and astrophysics to natural language processing and software engineering.