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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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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
Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
DOI
https://doi.org/10.3233/ifs-2010-0466
Collections
Department of Statistics, Article