Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)

On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs

Authors
Christian Moewes, Rudolf Kruse
Corresponding Author
Christian Moewes
Available Online August 2011.
DOI
https://doi.org/10.2991/eusflat.2011.46How to use a DOI?
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2011
ISBN
978-90-78677-00-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/eusflat.2011.46How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Christian Moewes
AU  - Rudolf Kruse
PY  - 2011/08
DA  - 2011/08
TI  - On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 943
EP  - 948
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.46
DO  - https://doi.org/10.2991/eusflat.2011.46
ID  - Moewes2011/08
ER  -