TABLE OF CONTENTS The problem of missing data in databases has recently been dealt with through the use computational intelligence. The hybrid of auto-associative neural networks and genetic algorithms has proven to be a successful approach to missing data imputation. Similarly, two auto-associative neural networks are developed to be used in conjunction with genetic algorithm to estimate missing data, and these approaches are compared to a Bayesian auto-associative neural network and genetic algorithm approach. One technique combines three neural networks to form a hybrid auto-associative network, while the other merges principal component analysis and neural networks. The hybrid of the neural network and genetic algorithm approach proves to be the most accurate when estimating one missing value, while a hybrid of principal component and neural networks is more consistent and captures patterns in the data more efficiently.
As explained in earlier chapters, the occurrence of missing data in databases, such as those that house medical data, for example ante-natal HIV data, is a common problem (Schafer and Graham, 2002; Nelwamondo & Marwala, 2007). Missing data may come about as a consequence of inefficiencies in the data acquisition or data storage processes. Non-responses to various fields in a questionnaire, a break in the transmission line or failure of hardware are some of the general causes of missing data (Abdella & Marwala, 2006). Many knowledge discovery and data analysis techniques for databases depend heavily on complete data (Fujikawa, 2001; Yim and Mitchell, 2005). For that reason, an effective method for tackling the problem...
