Dynamically weighted ensemble neural networks for classification

Combining the outputs of several neural networks into an aggregate output often gives improved accuracy over any individual output. The set of networks is known as an ensemble or committee. This paper presents an ensemble method for classification that has advantages over other techniques for linear combining. Normally, the output of an ensemble is a weighted sum whose weights are fixed, having been determined from the training or validation data. Our ensembles are weighted dynamically, the weights determined from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight.