Feature selection using a multilayer perceptron . The feedforward network is preliminarily trained using all available features whose saliencies are subsequently determined. The method amounts to pretraining the MFNN to learn a specific supervised learning task using all avail4 able features and computing a saliency metric S j related to each individual feature. Many classification or function approximation algorithms exhibit greatly improved speed of convergence and generalization properties when called upon to operate using training sets of reduced dimensionality. According to this method, a saliency metric is defined that depends on the sensitivity of the trained network outputs with respect to its inputs.
Most measures use average squared or absolute derivatives.
Tenth of measures based on derivatives have been proposed, and many others could be defined, we thus give below only a representative sample of these measures. Feature Selection Using a MultiLayer Perceptron . In J. Neural Network Comput. A benchmark to compare feature saliency is a probability of error criterion, denoted by P e , which computes the probability of error when only a single feature is used. We use Bayes error rate as a measure of saliency.