Abstract
An extension of Cellular Genetic Programming for data classifiation 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.
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Folino, G., Pizzuti, C., Spezzano, G. (2003). Ensemble Techniques for Parallel Genetic Programming Based Classifiers. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_6
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DOI: https://doi.org/10.1007/3-540-36599-0_6
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