Experts' Boasting in Trainable Fusion Rules

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.

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