DocumentCode :
2256431
Title :
A comparative study of four fuzzy integrals for classifier fusion
Author :
Feng, Hui-min ; Li, Xue-Fei ; Chen, Jun-Fen
Author_Institution :
Key Lab. of Machine Learning & Comput., Intell., Hebei Univ., Baoding, China
Volume :
1
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
332
Lastpage :
338
Abstract :
Fuzzy Integral is widely accepted and applied in multi-classifier fusion to express the importance of individual classifiers and the interaction among classifiers. In this fusion model, there are two keys to determine. One is determining the fuzzy measure. Many researchers have done much work and proposed many types of fuzzy measure and methods to determine fuzzy measures. Another is selecting from four types of fuzzy integral: Sugeno integral, Choquet integral, upper integral and lower integral. Usually, the type of fuzzy integral is specified in advance. Choquet integral is often the choice. This paper is to compare comprehensively four fuzzy integrals in multiple-classifier fusion and hope to give the foundation for selecting Choquet integral. According the theoretical and experimental analysis, it gives the conclusion that Choquet integral is the best suitable for classifier fusion.
Keywords :
fuzzy set theory; integral equations; pattern classification; sensor fusion; Choquet integral; Sugeno integral; comparative study; four fuzzy integrals; fusion model; fuzzy measure; individual classifiers; lower integral; multiclassifier fusion; multiple-classifier fusion; upper integral; Artificial neural networks; Cybernetics; Density measurement; Equations; Integral equations; Machine learning; Mathematical model; Classifier; Fusion; Fuzzy integral; Fuzzy measure; Nonnegative set function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
Type :
conf
DOI :
10.1109/ICMLC.2010.5581040
Filename :
5581040
Link To Document :
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