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 expertsʹ decisions. If the training set is utilized to design both the experts and the fusion rule, the expertsʹ outputs become too self-assured. In small sample situations, "optimistically biased" expertsʹ outputs bluffs the fusion rule designer. If the experts differ in complexity and in classification performance, then the expertsʹ boasting effect and can severely degrade the performance of a multiple classification system. Theoretically-based and experimental procedures are suggested to reduce the expertsʹ boasting effect.