Maximum Expectation Algorithms for Missing Data Estimation

Maximum Expectation Algorithms for Missing Data Estimation

Tshilidzi Marwala (University of Witwatersrand, South Africa)
DOI: 10.4018/978-1-60566-336-4.ch004
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Abstract

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 GMMEM 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.

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