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

Metadata

Show full item record

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

Item Usage Stats

219
views

0
downloads

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

Suggestions

OpenMETU
Core

Estimation and hypothesis testing in multivariate linear regression models under non normality İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01) This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Regression analysis with a dtochastic design variable Sazak, HS; Tiku, ML; İslam, Muhammed Qamarul (Wiley, 2006-04-01) In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.
Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models Yozgatlıgil, Ceylan (Informa UK Limited, 2009-11-01) Time series often contain observations of several variables and multivariate time series models are used to represent the relationship between these variables. There are many studies on vector autoregressive moving average (VARMA) models, but the representation of multiplicative seasonal VARMA models has not been seriously studied. In a multiplicative vector model, such as a seasonal VARMA model, the representation is not unique because of the noncommutative property of matrix multiplication. In this articl...
Multiple linear regression model with stochastic design variables İslam, Muhammed Qamarul (Informa UK Limited, 2010-01-01) In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.
Marginalized transition random effect models for multivariate longitudinal binary data İlk Dağ, Özlem (Wiley, 2007-03-01) Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, the computation and interpretation of marginal covariate effects can be difficult. This led Heagerty (1999, 2002) to propose models for longitudinal binary data in which a logistic regression is first used to explain the average marginal response. The model is then completed by introducing a conditional regression that allows for the longitudinal, within-subject, dependence, either...

Citation Formats

G. ŞİKKELİ, G. KÖKSAL, İ. Batmaz, and Ö. TÜRKER BAYRAK, “Classification models based on Tanaka’s fuzzy linear regression approach: The case of customer satisfaction modeling,” JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, pp. 341–351, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39746.