Techniques for Dealing with Missing Values in Feedforward Networks
Peter W. Vamplew, David Glenn Clark, Anthony I. Adams, J. Muench · UTAS Research Repository · 1996
: Missing or incomplete data, a common reality, causes problems for artificial neural networks. In this paper we investigate several methods for dealing with missing values in feedforward networks. Reduced networks, substitution, estimation and expanded networks are applied to three data sets. We find that data sets vary in their sensitivity to missing values, and that reduced networks and estimation are the most effective ways of dealing with them. Introduction Artificial neural networks trained using backpropagation have been used for a wide variety of classification problems. In many real world problems, however, some of the data may be missing or incomplete. This causes particular problems for artificial neural networks as the distributed nature of the processing makes it very difficult to isolate effects due to one variable. The aim of this study is to compare different techniques for dealing with missing data. We assume a complete set of training data is available and a single h...