Ensemble techniques for parallel genetic programming based classifiers

Authors:
Gianluigi Folino;Clara Pizzuti;Giandomenico Spezzano
Affiliations:
ICAR-CNR, Rende, CS, Italy;ICAR-CNR, Rende, CS, Italy;ICAR-CNR, Rende, CS, Italy
Venue:
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Year:
2003

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Abstract

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.