An extension of Cellular Genetic Programming for data classification with the boosting technique is presented and a comparison with the bagging-like majority voting approach is performed. The method is able to deal with large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training data. Experiments showed that, by using a sample of reasonable size, the extension with these voting algorithms enhances classification accuracy at a much lower computational cost. © Springer-Verlag 2004.
CITATION STYLE
Folino, G., Pizzuti, C., & Spezzano, G. (2004). Boosting technique for combining cellular GP classifiers. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3003, 47–56. https://doi.org/10.1007/978-3-540-24650-3_5
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