Classification models based on Tanaka's fuzzy linear regression approach: The case of customer satisfaction modeling

Date

2010-01-01

Author

ŞİKKELİ, GİZEM
KÖKSAL, GÜLSER
Batmaz, İnci
TÜRKER BAYRAK, ÖZLEM

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

General Engineering, Statistics and Probability, Artificial Intelligence

URI

https://hdl.handle.net/11511/39746

Collections

Department of Statistics, Article