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

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 parame...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2019; H. 2019; S. 1 - 9
Hauptverfasser: Gao, Pingping, Gao, Yabin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 2019
Hindawi
John Wiley & Sons, Inc
Schlagworte:
Clinical trials
Data analysis
Deletion
Economic models
Error detection
Euclidean geometry
Fuzzy sets
Information science
Mathematical programming
Methods
Outliers (statistics)
Parameter estimation
Quadrilaterals
Regression analysis
Regression models
Variables
ISSN:1024-123X, 1563-5147
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/4914593