Variable Screening

Variable screening aims to efficiently reduce the number of variables considered in a complex analysis, improving computational speed and potentially prediction accuracy. Current research focuses on developing and applying diverse screening algorithms, including machine learning models like convolutional neural networks and lasso regression, within various contexts such as medical imaging analysis and optimization problems like unit commitment. This work is significant because effective variable screening can accelerate analyses, enhance model performance, and lead to more efficient resource allocation across diverse fields, from healthcare to energy management.