Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce rules, define the corresponding system. A transformation of derived rules into interval type-2 is provided as well. fuzzification applied optimized with respect footprint uncertainty sets. process related Mamdani type inference. method proposed was evaluated by F-score measure benchmark