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Erschienen in:
2003 | OriginalPaper | Buchkapitel
Ensemble Techniques for Parallel Genetic Programming Based Classifiers
verfasst von : Gianluigi Folino, Clara Pizzuti, Giandomenico Spezzano
Erschienen in: Genetic Programming
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.