Missing Values in a Backpropogation Neural Net

An empirical study of methods of handling missing values in a backpropagation neural network is presented. Neural networks can be applied to many real world systems to perform classification, pattern recognition or prediction on the basis of input data. However, many such applications cannot guarantee that the data provided to the network will be complete. The backpropagation network does not lend itself easily to dealing with missing values due to its distributed nature of operation and the soft thresholds used in its nodes. Two common methods of handling this situation are tested and four new methods are proposed and