improving cooperative gp ensemble with 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. The method proposed runs on a distributed hybrid multi-island environment that combines the island and cellular models of parallel genetic programming. 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, up to 20 of pruning, ensemble accuracy increases. Experiments on several data sets show that combining clustering and pruning enhances classification accuracy of the ensemble approach.
ensemble classification genetic programming data mining