Open AccessQuadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection
Author(s) -
Pingping Gao,
Yabin Gao
Publication year - 2019
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2019/4914593
Subject(s) - outlier , mathematics , anomaly detection , quadrilateral , euclidean distance , regression analysis , fuzzy logic , interval (graph theory) , statistics , robust regression , data mining , artificial intelligence , computer science , engineering , combinatorics , geometry , structural engineering , finite element method
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.