Training Distributed GP Ensemble with a Selective Algorithm based on Clustering and Pruning for Pattern Classification

A boosting algorithm based on cellular genetic programming to
build an ensemble of predictors is proposed. The method evolves a
population of trees for a fixed number of rounds and, after each
round, it chooses the predictors to include into the ensemble by
applying a clustering algorithm to the population of classifiers.
Clustering the population allows the selection of the most diverse
and fittest trees that best contribute to improve classification
accuracy. The method proposed runs on a distributed hybrid
environment that combines the island and cellular models of
parallel genetic programming. The combination of the two models
provides an efficient implementation of distributed GP, and, at
the same time, the generation of low sized and accurate decision
trees. The large amount of memory required to store the ensemble
makes the method heavy to deploy. The paper shows that, by
applying suitable pruning strategies, it is possible to select a
subset of the classifiers without increasing misclassification
errors; indeed, for some data sets, up to 30\% of pruning,
ensemble accuracy increases. Experimental results show that the
combination of clustering and pruning enhances classification
accuracy of the ensemble approach.