TABLE OF CONTENTS Two sets of hybrid techniques have recently emerged for the imputation of missing data. These are, first, the combination of the Gaussian Mixtures Model and the Expectation Maximization algorithms (the GMM-EM) and second, the combination of Auto-Associative Neural Networks with Evolutionary Optimization (the AANN-EO). In this chapter, the evolutionary optimization method implemented is the particle swarm optimization method (the AANN-PSO). Both the GMM-EM and AANN-EO techniques have been discussed individually and their merits discussed at length in the available literature. This chapter provides a comparison between these techniques, using datasets from an industrial power plant, an industrial winding process and an HIV sero-prevalence survey. The results show that GMM-EM method is suitable and performs better in cases where there is little or no interdependency between the input variables, whereas the AANN-PSO combination is suitable when there are inherent nonlinear relationships between some of the given variables.
Databases, such as those that store measurement or medical data may become subject to missing values in either the data acquisition or data-storage process. Problems in a sensor, a break in the data transmission line or non-response to questions posed in a questionnaire are prime examples of how data can go missing. The problem of missing data creates a difficulty in the analysis and decision-making processes that depend on the data to be in a complete form and, thereby, they require methods of estimation that are accurate and efficient. Various techniques exist as a solution to this problem, ranging from data deletion...
