Determining the relevance of input features for multilayer perceptrons

This paper presents an approach to determine the relevance of individual input attributes for trained Multilayer Perceptrons (MLPs). To reflect the impact of an input attribute on the output of an MLP, the relevance is aimed at representing the output sensitivity of the MLP to the attribute variation. The sensitivity is defined as the mathematical expectation of output deviations of an MLP due to its input deviation with respect to overall input patterns. The basic idea for the introduction of such a relevance measure is that a well-trained MLP can capture salient features of the problem it deals with and thus become more sensitive to those input attributes that make more contributions to the MLP´s behavior. The relevance can be employed as a relative criterion for assessing individual input attributes. The results from the experiments on two typical problems demonstrate the effectiveness of the relevance in identifying irrelevant input attribute.