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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...
It has been shown that the fuzzy integral is an effective tool for the fusion of multiple classifiers. Of primary importance in the development of the system is the choice of the measure which embodies the importance of subsets of classifiers. In this paper we propose a method for a dynamic fuzzy measure which will change following the pattern to be classified (data dependent). This method uses the...
In finite set, Choquet fuzzy integral with respect to fuzzy measures can be transferred into linear combination of product, based on this fact we can choose standard optimization technical to determine fuzzy measures. This paper present linear programming and quadratic programming to determine fuzzy measures, the experiments demonstrate that classification accuracy of fuzzy integral with respect to...
Fuzzy integral is a valid method for combining multiple classifiers. In classifier fusion system based on fuzzy integral, the fuzzy measure will have much impact on system's performance. Also many authors have done researches on how to determine the fuzzy measure. Our paper presents some new opinions about classifier fusion based on fuzzy integral and gives the lower bound of error rate of fusion...
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