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JACIII Vol.23 No.3 pp. 571-576
doi: 10.20965/jaciii.2019.p0571
(2019)

Paper:

# Kernel Fuzzy c-Regression Based on Least Absolute Deviation with Modified Huber Function

## Yusuke Oi* and Yasunori Endo**

*Department of Risk Engineering, Graduate School of Systems and Information Engineering, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

**Faculty of Engineering, Information and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

December 20, 2017
Accepted:
January 18, 2019
Published:
May 20, 2019
Keywords:
fuzzy c-regression, kernel method, least absolute deviation
Abstract

A result by the proposed method

The fuzzy c-regression models are useful for datasets with various correlations. To deal with nonlinear datasets, a kernel fuzzy c-regression (KFCR) method was previously proposed. However, this method is weak for outliers because its objective function is based on the least square principle. We introduce the least absolute deviation (LAD) method with a modified Huber function into the KFCR (LAD-KFCR) to overcome the abovementioned problem. We verify the usefulness of the proposed LAD-KFCR method through numerical examples.

Yusuke Oi and Yasunori Endo, “Kernel Fuzzy c-Regression Based on Least Absolute Deviation with Modified Huber Function,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 571-576, 2019.
Data files:
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Last updated on Dec. 02, 2021