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

DOI

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.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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 (EUSFLAT-11)
PB  - Atlantis Press
SP  - 943
EP  - 948
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.46
DO  - 10.2991/eusflat.2011.46
ID  - Moewes2011/08
ER  -