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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
U.M. Fayyad, G. Piatesky-Shapiro and P. Smith (1996). From Data Mining to Knowledge Discovery: an overview. In U.M. Fayyad & al. (Eds) Advances in Knowledge Discovery and Data Mining, pp. 1–34, AAAI/MIT Press.
G. Folino, C. Pizzuti and G. Spezzano (1999). A Cellular Genetic Programming Approach to Classification. Proc. Of the Genetic and Evolutionary Computation Conference GECCO99, Morgan Kaufmann, pp. 1015–1020, Orlando, Florida.
J. R. Koza (1992). Genetic Programming: On Programming Computers by Means of Natural Selection and Genetics, MIT Press.
J. Ross Quinlan (1993). C4-5 Programs for Machine Learning. San Mateo, Calif.: Morgan Kaufmann.
T. Toffoli and N. Margolus (1986). Cellular Automata Machines A New Environment for Modeling. The MIT Press, Cambridge, Massachusetts.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media New York
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-1-4615-4489-0_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7023-9
Online ISBN: 978-1-4615-4489-0
eBook Packages: Springer Book Archive