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Iterative fuzzy support vector machine classification
conference contribution
posted on 2007-01-01, 00:00 authored by Alistair ShiltonAlistair Shilton, D T H LaiFuzzy support vector machine (FSVM) classifiers are a class of nonlinear binary classifiers which extend Vapnik's support vector machine (SVM) formulation. In the absence of additional information, fuzzy membership values are usually selected based on the distribution of training vectors, where a number of assumptions are made about the underlying shape of this distribution. In this paper we present an alternative method of generating membership values which we call iterative FSVM (I-FSVM). Our method generates membership values iteratively based on the positions of training vectors relative to the SVM decision surface itself. We show that our algorithm is capable of generating results equivalent to an SVM with a modified (non distance based) penalty (risk) function. Experiments have been carried out on three real world binary classification problems taken from the UCI repository, namely the spambase dataset and the adult (census) dataset.
History
Event
Fuzzy Systems. Conference (2007 : London, Eng.)Series
Fuzzy Systems ConferencePagination
1 - 6Publisher
Institute of Electrical and Electronics EngineersLocation
London, Eng.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2007-07-23End date
2007-07-26ISSN
1098-7584ISBN-13
9781424412105ISBN-10
1424412102Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
[Unknown]Title of proceedings
FUZZ-IEEE : Proceedings of the 2007 IEEE International Conference on Fuzzy SystemsUsage metrics
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