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

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  • Pingping Gao
  • Yabin Gao

Abstract

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.

Suggested Citation

  • Pingping Gao & Yabin Gao, 2019. "Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-9, August.
    • Handle: RePEc:hin:jnlmpe:4914593
    DOI: 10.1155/2019/4914593
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    Cited by:

    1. Marjana Čubranić-Dobrodolac & Libor Švadlenka & Svetlana Čičević & Aleksandar Trifunović & Momčilo Dobrodolac, 2020. "Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents," Mathematics, MDPI, vol. 8(9), pages 1-19, September.

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