Feature-based decision aggregation in modular neural network classifiers

In several modular neural network (MNN) architectures, the individual decis ions at the module level have to be integrated together using a voting sche me. All these voting schemes use the outputs of the individual modules to p roduce a global output without inferring explicit information from the prob lem feature space. This makes the choice of the aggregation procedure very subjective. In this work, a new MNN architecture will be presented. This ar chitecture integrates learning into the voting scheme. We will be focusing on making the decision fusion a more dynamic process. In this context, dyna mic means the aggregation procedure which has the flexibility to adapt to c hanges in the input. This approach requires the aggregation procedure to ga ther information about the input to help better understand how to dynamical ly aggregate decisions. (C) 1999 Published by Elsevier Science B.V. All rig hts reserved.