title:
 
On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs
publication:
 
EUSFLAT
part of series:
  Advances in Intelligent Systems Research
volume-issue:   1 - 1
pages:   943 - 948
ISBN:
  978-90-78677-00-0
ISSN:
  1951-6851
DOI:
  doi:10.2991/eusflat.2011.46 (how to use a DOI)
author(s):
 
Christian Moewes, Rudolf Kruse
publication date:
 
July 2011
keywords:
 
Classification, fuzzy rule-based classifiers, fuzzy SVM, SVM
abstract:
 
In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trends and conclude with more promising alternatives.
copyright:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: