Bayesian selection of important features for feedforward neural networks

Abstract This paper presents a probability of error based method of determining the saliency (usefulness) of input features and hidden nodes. We show that the partial derivative of the output nodes with respect to a given input feature yields a sensitivity measure for the probability of error. This partial derivative provides a saliency metric for determining the sensitivity of the feedforward network trained with a mean squared error learning procedure to a given input feature.

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