Granular Approximation

Granular approximation is a computational approach that represents data as collections of "granules," or clusters of similar data points, to simplify complex datasets and improve the efficiency and interpretability of machine learning models. Current research focuses on developing improved algorithms for constructing these granular approximations, particularly using fuzzy sets and variable precision rough sets, and exploring their application in classification tasks, including multi-class problems. This work aims to enhance the transparency and explainability of machine learning, leading to more reliable and understandable predictions across diverse applications such as image segmentation and cluster validation.