TABLE OF CONTENTS Missing data creates various problems in analyzing and processing data in databases. In this chapter, a method aimed at approximating missing data in a database that uses a combination of genetic algorithms and neural networks is introduced. The presented method uses genetic algorithms to minimize an error function derived from an auto-associative neural network. The Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks are employed to form an auto-associative network. An investigation is undertaken into using the method to predict missing data accurately as the number of missing cases within a single record increases. It is observed that there is no significant reduction in the accuracy of the results as the number of missing cases in a single record increases. It is also found that results obtained from using the MLP are better than from the RBF for the data used.
Inferences made from available data for many applications depend on the completeness and the quality of the data being used in the analysis. Therefore, inferences made from complete data are most likely to be more accurate than those made from incomplete data are. However, there are time critical applications that necessitate estimation or approximation of the values of some missing variables, which have to be supplied with the values of other corresponding variables. Such situations may appear in a system that uses a number of instruments, where one or more of the sensors used in the system fail. In such a situation, the values from the...
