Entropy-based feature selection with applications to industrial internet of things (IoT) and breast cancer prediction

Feature Selection (FS) is employed in the Machine Learning (ML) process to increase accuracy. Eliminating redundant and irrelevant variables while keeping the most important ones boosts the prediction capacity of the algorithms. FS is essential because of this. The current paper delves into entropy-based FS, which emphasizes the phenomenal role of entropy in developing numerous interdisciplinary fields of human knowledge, including ML. More potentially, some significant applications of entropy-based FS to the Internet of Things (IoT) and breast cancer prediction are provided.