Abstract
A parallel genetic programming approach to data classification is presented. The method uses cellular automata as a framework to enable a fine-grained parallel implementation of GP through the grid model. Experiments on real datasets from the UCI machine learning repository show good results with respect to C4.5. The generated trees are smaller, they have a misclassification error on the training set comparable, but, more important, they generalise better than C4.5. Furthermore, performance results show a nearly linear speedup.
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Folino, G., Pizzuti, C., Spezzano, G. (2000). Scalable Classification of Large Data Sets by Parallel Genetic Programming. In: Kacsuk, P., Kotsis, G. (eds) Distributed and Parallel Systems. The Springer International Series in Engineering and Computer Science, vol 567. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4489-0_11
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DOI: https://doi.org/10.1007/978-1-4615-4489-0_11
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