Dynamically weighted ensemble neural networks for regression problems

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. This work presents an ensemble method for regression that has advantages over simple weighted or weighted average combining techniques. Generally, the output of an ensemble is a weighted sum whose weights are fixed. Our ensemble is weighted dynamically, the weights dynamically determined from the predicting accuracies of the trained networks with training dataset. The more accurate a network seems to be of its prediction, the higher the weight. This is implemented by generalized regression neural network. Empirical results show that this method improved on predicting accuracy.