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

Applied Sciences

Adam Chlopowiec¹  Adam Dlubak¹  Martin Tabakov¹  Adrian Chlopowiec¹ 

[1] Department of Computational Intelligence, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland;

关键词: fuzzy sets;    interval type-2 fuzzy sets;    fuzzy information systems;    classification;    fuzzification optimization;   

DOI  :  10.3390/app11083484

来源: DOAJ

【 摘 要 】

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 fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

【 授权许可】

Unknown