TABLE OF CONTENTS This chapter introduces a committee of networks for estimating missing data. The first committee of networks consists of multi-layer perceptrons (MLPs), support vector machines (SVMs) and radial basis functions (RBFs). The committee was constructed from a weighted combination of these three networks. The second, third and fourth committees of networks were evolved using a genetic programming approach and used the MLPs, RBFs and SVMs, respectively. The committee of networks was collectively implemented with hybrid particle-swarm optimization and a genetic algorithm for missing data estimation. They were tested on an artificial taster as well as HIVdatasets and then compared to the individual multi-layer perception, radial basis functions and support vector regression for missing data estimation. It was found that the committee of network approach provided improved results over the three methods acting individually. However, this improvement comes with a higher computational load than does using the individual approaches. Furthermore, it is found that evolving a committee method was a good way of constructing a committee.
Several techniques have been introduced for missing data estimation (Abdella, 2005; Abdella & Marwala, 2005, 2006; Allison, 2000). A number of of these methods make use of machine learning approaches to accomplish this mission. In this chapter, committees of machine learning algorithms are used for missing data estimation. The principal incentive for using the committees of networks technique is from the intuitive logic that many 'heads' are better than one and, therefore, using many networks is thus better than using one.
Du, Zhai, and...
