Interval Valued Intuitionistic Fuzzy

Interval-valued intuitionistic fuzzy sets (IVIFSs) extend fuzzy set theory to represent uncertainty and imprecision more comprehensively by incorporating both membership and non-membership degrees, each represented as intervals. Current research focuses on applying IVIFSs to enhance decision-making processes, particularly in multi-attribute group decision-making (MAGDM) problems, often employing techniques like TOPSIS, optimization models, and novel similarity measures based on projection and cosine similarity. These advancements improve the handling of incomplete or uncertain information in various fields, including feature selection, machine learning (e.g., addressing class imbalance), and real-world applications like medical diagnosis and project selection. The resulting models offer more robust and nuanced approaches to complex decision problems compared to traditional methods.