Fuzzy Similarity-Driven Three-Way Decision Model of Rough Fuzzy Sets

Three-way decision (3WD) theory provides an innovative approach to categorizing uncertain issues into acceptance, rejection, and non-commitment regions. The rough fuzzy sets (RFS) model extends rough sets to handle imprecise or fuzzy concepts. A critical challenge in developing the three-way decision model of rough fuzzy sets (3WDRFS) lies in determining and interpreting threshold pairs. However, the traditional expertbased subjective risk parameters often lead to significant misclassification errors in 3WD. To address this limitation, we propose a novel fuzzy similarity-driven 3WDRFS framework (3WDRFSFS). Our approach involves two key steps: (1) developing a three-way approximation shadowed set (3WA-SS) model using average-step fuzzy sets (AFS), and (2) formulating an objective function to quantify the fuzzy similarity between AFS and 3WASS, which is utilized to derive the optimal threshold pair for the 3WDRFS-FS model. Relevant experimental results validate the effectiveness of our proposed model. Moreover, the 3WDRFSFS model demonstrates superior performance compared to the 0.5-approximation model (0.5-AM).