scholarly journals An Adaptive Fuzzy Regression Model for the Prediction of Dichotomous Response Variables

2007 ◽  
Author(s):  
Sarun Phibanchon ◽  
Sameem Abdul Kareem ◽  
Rosnah Zain ◽  
Basir Abidin ◽  
R.M. Dom
Keyword(s):  
Regression Model ◽  
Fuzzy Regression ◽  
Adaptive Fuzzy ◽  
Fuzzy Regression Model ◽  
Response Variables

scholarly journals Customer Satisfaction and Behaviour at Retail Outlets: an Adaptive Fuzzy Regression Model with LINGO Based Analysis

2020 ◽  
pp. 275-285
Author(s):  
Mohd Rizwanullah ◽  
Salah Abunar ◽  
Sayeeduzzafar Qazi
Keyword(s):  
Customer Satisfaction ◽  
Regression Model ◽  
Research Work ◽  
Response Surfaces ◽  
Customer Behavior ◽  
Fuzzy Regression ◽  
Adaptive Fuzzy ◽  
Fuzzy Regression Model ◽  
Retail Outlets

Increasing rivalry for-profit or non-profit is pushing companies to devote more and more attention to pleasing consumers with excellent quality customer services. This study aims to develop a model to analyze customer behavior in a retail store and provide accurate inference for decision making. Another critical objective for this research work is the adaptation of the faceted form of neuro-response, which is substituted by the Adaptive Fuzzy Logistic Regression Model (AFLRM). AFLRM has resulting benefits over Neuro-surface and Mean Demand Heuristic methods. A sample of 100 customers who visited or walked in the retails was used as a sample. Other than neuro-response surfaces (NRSM) and The Mean Demand Heuristic models (MDSM), the present study has accustomed a generalized form known as Adaptive Fuzzy Linear Regression Model (AFLRM) to deliver the benchmark for former models and give the highest level of accuracy for future behavior of a customer. LINGO based Markovian analysis has also been used with the above model to understand the behavior of the system under study. The significance of service and product attributes is implicitly derived via the fuzzy regression model for customer satisfaction measurement. It is observed that the critical gap between the quality of product and services and Customer Satisfaction is Product/Service Satisfaction, Motivation and Buying Experience, and Credibility and Security. The authors’ finding indicates that the effort of listening to the customer's voice should be more critical. Result analysis based on computational results concerning the questionnaire for measuring the customer behavior and the system validates the model under study. Appropriate, useful with reliable action plans for every critical product and service aspect can be developed by applying the adaptive regression methodology to control the quality of service or managing customer satisfaction, thereby providing executives with a competitive gain. Also explored the behavior of the system, i.e., whether the customer will move to the new retail outlets or they will remain in the same state by using the LINGO based software program model. Keywords: heuristic, fuzzy, Markov process, retail customer, customer behavior, LINGO, ISM.

scholarly journals Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Pingping Gao ◽  
Yabin Gao
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Outlier Detection ◽  
Euclidean Distance ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis ◽  
Fuzzy Regression Model ◽  
Interval Type

This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.

Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model

2017 ◽  
Vol 37 (2) ◽  
pp. 281-289 ◽  
Author(s):  
Narges Shafaei Bajestani ◽  
Ali Vahidian Kamyad ◽  
Ensieh Nasli Esfahani ◽  
Assef Zare
Keyword(s):  
Regression Model ◽  
Fuzzy Regression ◽  
Diabetic Patients ◽  
Fuzzy Regression Model
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