Interval piecewise regression model with automatic change-point detection by quadratic programming

Jing Rung Yu*, Gwo Hshiung Tzeng, Han-Lin Li

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.

Original languageEnglish
Pages (from-to)347-361
Number of pages15
JournalInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
Volume13
Issue number3
DOIs
StatePublished - 1 Jun 2005

Keywords

  • Change-point
  • Fuzzy regression
  • Necessity
  • Piecewise regression
  • Possibility
  • Quadratic programming

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