Affiliations: Department of Industrial Engineering, Middle East Technical University, Ankara, Turkey | Department of Statistics, Middle East Technical University, Ankara, Turkey | Department of Industrial Engineering, Çankaya University, Ankara, Turkey
Note: [] Corresponding author. Gülser Köksal, Department of Industrial Engineering, Middle East Technical University, 06531 Ankara, Turkey. Tel.: +90 312 2102285; Fax: +90 312 2104786; E-mail: [email protected]
Abstract: Fuzzy linear regression (FLR) approaches are widely used for modeling relations between variables that involve human judgments, qualitative and imprecise data. Tanaka’s FLR analysis is the first one developed and widely used for this purpose. However, this method is not appropriate for classification problems, because it can only handle continuous type dependent variables rather than categorical. In this study, we propose three alternative approaches for building classification models, for a customer satisfaction survey data, based on Tanaka’s FLR approach. In these models, we aim to reflect both random and fuzzy types of uncertainties in the data in different ways, and compare their performances using several classification performance measures. Thus, this study contributes to the field of fuzzy classification by developing Tanaka based classification models.
Keywords: Fuzziness, fuzzy classification, fuzzy linear regression (FLR), customer satisfaction
