Feature Selection Using a Multilayer Perceptron

Abstract

The problem of selecting the best set of features for target recognition using a multilayer perceptron is addressed in this paper. A technique has been developed which analyzes the weights in a multilayer perceptron to determine which features the network finds important and which are unimportant. A brief introduction to the use of multilayer perceptrons for classification and the training rules available is followed by the mathematical development of the saliency measure for multilayer perceptrons. The technique is applied to two different image databases and is found to be consistent with statistical techniques and independent of the network initial conditions. The saliency measure is then used to compare the results of two different training rules on a target recognition problem. 1 Introduction Recently there has been a great deal of interest in the use of multilayer perceptrons as classifiers in pattern recognition problems (see, for example, [11]). Unfortunately, little ...