Fuzzy One-Class Classification Model Using Contamination Neighborhoods

A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that a...

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Bibliographic Details
Published in:Advances in fuzzy systems 2012-01, Vol.2012 (2012), p.1-10
Main Author: Utkin, Lev V.
Format: Article
Language:English
Subjects:
Accuracy
Classification
Contamination
Data points
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Mathematical models
Methods
Probability distribution
Risk
Studies
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Summary:A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.
ISSN:1687-7101
1687-711X
DOI:10.1155/2012/984325