Algorithms in sequential fuzzy regression models based on least absolute deviations

Authors:
Hengjin Tang;Sadaaki Miyamoto
Affiliations:
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan;Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Venue:
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Year:
2010

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Abstract

The method of fuzzy c-regression models is known to be useful in real applications, but there are two drawbacks. First, the results have a strong dependency on the predefined number of clusters. Second, the method of least squares is frequently sensitive to outliers or noises. To avoid these drawbacks, we apply a method of sequentially extracting one cluster at a time using noise-detecting method to fuzzy c-regression models which enables an automatic determination of clusters. Moreover regression models are based on least absolute deviations (FCRMLAD) which are known to be robust to noises. We show the effectiveness of the proposed method by using numerical examples.