Voting schemes for cooperative neural network classifiers

Multiple neural network modules cooperating in taking a classification decision are modeled as multiple voters electing one candidate in a single ballot election assuming the availability of votes´ preferences and intensities. All modules are considered as candidates as well as voters. Voting bids are the output-activations of the modules forming the cooperative modular structure. Different voting schemes are compared according to the accuracy of defining classification decision boundaries. A higher classification accuracy implies a better representation of the information available at different preferences (output values). The effect of the modules´ voting power on the accuracy of the decision is studied and integrated in the network´s design strategy