A Novel Reliable Three-Way Multiclassification Model Under Intuitionistic Fuzzy Environment
Libo Zhang, Cong Guo, Tianxing Wang, Dun Liu, Huaxiong Li · IEEE Transactions on Fuzzy Systems · 2025
Three-way decision (3WD) is a trisecting-and-acting model that is in line with the decision-making logic of human beings. Bayesian risk decision (BRD) theory is a statistical method that optimizes decisions by quantifying and minimizing uncertainty-induced losses. As a typical BRD method, cost-sensitive 3WD aims to reduce decision risks and enhance decision credibility by incorporating an uncertainty decision item, known as boundary decision. However, in most existing 3WD-based multiclassification (MC) models, there is redundancy in both the decision item set and cost matrix, which may lead to decision conflict, ambiguity, and redundancy. Moreover, determining the cost matrix under the intuitionistic fuzzy (IF) environment remains a challenging task. To address these problems, we propose a reliable three-way multiclassification (R3WMC) model under the IF environment. Specifically, an IF cost transformation mechanism is proposed, and a novel boundary decision associated with the corresponding compact cost matrix is established to achieve a conflict-free decision set. Motivated by confidence-based classifiers, we propose an effective decision rule which is time-efficient and able to solve decision conflicts. We also demonstrate that our model is an enhanced BRD model, contributing to a solid theoretical basis for the proposed R3WMC method. Finally, a comparative analysis of multiple models across various datasets validates the reliability and superior performance of our model.