An extension of Cellular Genetic Programming for data<BR>classification to induce an ensemble of predictors is presented.<BR>Each classifier is trained on a different subset of the overall<BR>data, then they are combined to classify new tuples by applying a<BR>simple majority voting algorithm, like bagging. Preliminary<BR>results on a large data set show that the ensemble of classifiers<BR>trained on a sample of the data obtains higher accuracy than a<BR>single classifier that uses the entire data set at a much lower<BR>computational cost.
Ensemble Techniques for Parallel Genetic Programming based Classifiers
Folino Gianluigi;Pizzuti Clara;Spezzano Giandomenico
2003
Abstract
An extension of Cellular Genetic Programming for dataclassification 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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