DocumentCode :
1937162
Title :
A Novel Fuzzy Classifier Ensemble System
Author :
Yang, Ai-Min ; Jiang, Ling-Min ; Li, Xin-Guang ; Zhou, Yong-Mei
Author_Institution :
Guangdong Univ. of Foreign Studies, Guangzhou
Volume :
6
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
3582
Lastpage :
3587
Abstract :
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.
Keywords :
fuzzy set theory; pattern classification; classifier reliability; classifier sets generalization difference; fuzzy classifier ensemble system; membership matrix; Convolution; Data visualization; Filters; Fuzzy systems; Image generation; Machine learning; Noise generators; Oceans; Streaming media; Vectors; Classifier ensemble; Classifier´s reliability; Fuzzy classifier; Generalization difference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370768
Filename :
4370768
Link To Document :
بازگشت