Kernel Fuzzy <i>c</i>-Regression Based on Least Absolute Deviation with Modified Huber Function

• Oi Yusuke

  Department of Risk Engineering, Graduate School of Systems and Information Engineering, University of Tsukuba

• Endo Yasunori

  Faculty of Engineering, Information and Systems, University of Tsukuba

Bibliographic Information

Other Title

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

Abstract

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.

Journal

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 23 (3), 571-576, 2019-05-20

  Fuji Technology Press Ltd.

Keywords

• fuzzy c-regression
• kernel method
• least absolute deviation

Details

• CRID: 1390282763115001856
• NII Article ID: 130007651644
• NII Book ID: AA12042502
• DOI: 10.20965/jaciii.2019.p0571
• ISSN: 18838014
• ISSN: 13430130
• NDL BIB ID: 029673437
• Web Site: http://id.ndl.go.jp/bib/029673437
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I029673437
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII002300030024
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed