Parsimonious network design and feature selection through node pruning

Proposes a node saliency measure and a backpropagation type of algorithm to compute the node saliencies. A node-pruning procedure is then presented to remove insalient nodes in the network to create a parsimonious network. The optimal/suboptimal subset of features are simultaneously selected by the network. The performance of the proposed approach for feature selection is compared with Whitney´s feature selection method. One advantage of the node-pruning procedure over classical feature selection methods is that the node-pruning procedure can simultaneously “optimize” both the feature set and the classifier, while classical feature selection methods select the “best” subset of features with respect to a fixed classifier