Intuitionistic Fuzzy

Intuitionistic fuzzy sets (IFSs) extend traditional fuzzy sets by incorporating both membership and non-membership degrees, allowing for the modeling of uncertainty and hesitation in data. Current research focuses on applying IFSs to enhance various machine learning models, including support vector machines, random forests, broad learning systems, and neural networks, often to improve robustness against noise and outliers or to increase interpretability. This work demonstrates the value of IFSs in handling imprecise information and improving the performance and reliability of decision-making systems across diverse fields like image classification, medical diagnosis, and fault detection. The resulting models often exhibit improved accuracy and offer enhanced explainability, leading to more trustworthy and effective applications.