A Novel Fuzzy Classifier Ensemble System

In this paper, a novel fuzzy classifier ensemble system is proposed. This system can reduce subjective factor in building a fuzzy classifier, and improve the classification recognition rate and stability. Three proposed approaches are introduced, namely, the approach of measuring generalization diff...

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Bibliographic Details
Published in2007 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3582 - 3587
Main Authors Ai-Min Yang, Ling-Min Jiang, Xin-Guang Li, Yong-Mei Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Classifier ensemble
Classifier's reliability
Convolution
Data visualization
Filters
Fuzzy classifier
Fuzzy systems
Generalization difference
Image generation
Machine learning
Noise generators
Oceans
Streaming media
Vectors
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Summary:In this paper, a novel fuzzy classifier ensemble system is proposed. This system can reduce subjective factor in building a fuzzy classifier, and improve the classification recognition rate and stability. Three proposed approaches are introduced, namely, the approach of measuring generalization difference(GD) of classifier sets to select individual classifiers, the approach of determining individual classifier's reliability by the proposed membership matrix, the approach of classifier ensemble. The proposed system is evaluated with standard data sets. The comparison of experiments and the existed classifier ensemble systems. The experiment results show that the recognition rate of our proposed system is higher than ones of other classifier ensemble systems.
ISBN:1424409721
9781424409723
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370768