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

Yusuke Oi | Zendy; Yasunori Endo | Zendy

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

Author(s) -

Yusuke Oi,

Yasunori Endo

Publication year - 2019

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2019.p0571

Subject(s) - least absolute deviations , outlier , computer science , absolute deviation , kernel (algebra) , fuzzy logic , regression , robust regression , kernel method , function (biology) , kernel regression , mathematics , artificial intelligence , algorithm , statistics , support vector machine , combinatorics , evolutionary biology , biology

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.

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