Fuzzy Change-Point Algorithms for Regression Models

Shao-Tung Chang, Kang Ping Lu, Miin Shen Yang

研究成果: 雜誌貢獻文章同行評審

15 引文 斯高帕斯(Scopus)

摘要

Change-point (CP) regression models have been widely applied in various fields, where detecting CPs is an important problem. Detecting the location of CPs in regression models could be equivalent to partitioning data points into clusters of similar individuals. In the literature, fuzzy clustering has been widely applied in various fields, but it is less used in locating CPs in CP regression models. In this paper, a new method, called fuzzy CP (FCP) algorithm, is proposed to detect the CPs and simultaneously estimate the parameters of regression models. The fuzzy c -partitions concept is first embedded into the CP regression models. Any possible collection of all CPs is considered as a partitioning of data with a fuzzy membership. We then transfer these memberships into the pseudomemberships of data points belonging to each individual cluster, and therefore, we can obtain the estimates for model parameters by the fuzzy c-regressions method. Subsequently, we use the fuzzy c -means clustering to obtain the new iterates of the CP collection memberships by minimizing an objective function concerning the deviations between the predicted response values and data values. We illustrate the new approach with several numerical examples and real datasets. Experimental results actually show that the proposed FCP is an effective and useful CP detection algorithm for CP regression models and can be applied to various fields, such as econometrics, medicine, quality control, and signal processing.

原文英語
文章編號7081744
頁(從 - 到)2343-2357
頁數15
期刊IEEE Transactions on Fuzzy Systems
23
發行號6
DOIs
出版狀態已發佈 - 2015 十二月 1

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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