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 HIV datasets and then compared to the individual multi-layer perceptron, 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.
Information Theoretic Models for Dependence Analysis And missing Data Estimation
