Ensemble Techniques for Parallel Genetic Programming based Classifiers

An extension of Cellular Genetic Programming for data
classification to induce an ensemble of predictors is presented.
Each classifier is trained on a different subset of the overall
data, then they are combined to classify new tuples by applying a
simple majority voting algorithm, like bagging. Preliminary
results on a large data set show that the ensemble of classifiers
trained on a sample of the data obtains higher accuracy than a
single classifier that uses the entire data set at a much lower
computational cost.