Feature-based decision aggregation in modular neural network classifiers

In several modular neural network (MNN) architectures, the individual decisions at the module level have to be integrated together using a voting scheme. All these voting schemes use the outputs of the individual modules to produce a global output without inferring explicit information from the problem feature space. This makes the choice of the aggregation procedure very subjective. In this work, a new MNN architecture will be presented. This architecture integrates learning into the voting scheme. We will be focusing on making the decision fusion a more dynamic process. In this context, dynamic means the aggregation procedure which has the ̄exibility to adapt to changes in the input. This approach requires the aggregation procedure to gather information about the input to help better understand how to dynamically aggregate decisions. Ó 1999 Published by Elsevier Science B.V. All rights reserved.