A novel fuzzy knowledge graph pairs approach in decision making

Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records’ label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value ( k ∗ ) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems