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Experts' boasting in trainable fusion rules | IEEE Journals & Magazine | IEEE Xplore

Experts' boasting in trainable fusion rules


Abstract:

We consider the trainable fusion rule design problem when the expert classifiers provide crisp outputs and the behavior space knowledge method is used to fuse local exper...Show More

Abstract:

We consider the trainable fusion rule design problem when the expert classifiers provide crisp outputs and the behavior space knowledge method is used to fuse local experts' decisions. If the training set is utilized to design both the experts and the fusion rule, the experts' outputs become too self-assured. In small sample situations, "optimistically biased" experts' outputs bluffs the fusion rule designer. If the experts differ in complexity and in classification performance, then the experts' boasting effect and can severely degrade the performance of a multiple classification system. Theoretically-based and experimental procedures are suggested to reduce the experts' boasting effect.
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 25, Issue: 9, September 2003)
Page(s): 1178 - 1182
Date of Publication: 08 September 2003

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