Affiliations: [a] Department of Computational Intelligence, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland
| [b] Facultat d’Informática de Barcelona, Universitat Politècnica de Catalunya, Spain
Correspondence:
[*]
Corresponding author. Martin Tabakov, Department of Computational Intelligence, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland. E-mail: [email protected].
Abstract: In this research, we succeeded in introducing a new reasoning procedure which applies interval type-2 fuzzy sets into a rule induction process. Our proposal allows information granulation which resulted in achieving good experimental results. We introduced decision tables with elements assumed as interval type-2 fuzzy sets which greatly generalize information. Next, by applying corresponding rule induction procedure, we introduced the possibility to generate directly from a benchmark data fuzzy rulebases for type-2 fuzzy inference models. We strongly believe that our reasoning approach will be a proper solution for different research issues such as classification or ranking procedures as well as determining knowledge for fuzzy inference models. The method proposed was tested in a classification problem verified by using medical benchmark data.
Keywords: Fuzzy sets, fuzzy reasoning, interval type-2 fuzzy sets, data
classification, data discovery, pawlak’s information system, information granulation, rule
induction, fuzzy rules
