Abstract
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.
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References
Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, (36):105–139, 1999.
Leo Breiman. Bagging predictors. Machine Learning, 24(2):123–140, 1996.
Leo Breiman. Arcing classifiers. Annals of Statistics, 26:801–824, 1998.
Leo Breiman. Pasting small votes for classification in large databases and on-line. Machine Learning, 36(1,2):85–103, 1999.
P. K. Chan and S. J. Stolfo. A comparative evaluation of voting and meta-learning on partitioned data. In International Conference on Machine Learning ICML95, pages 90–98, 1995.
N. Chawla, T. E. Moore, W. Bowyer K, L. O. Hall, C. Springer, and P. Kegelmeyer. Bagging-like effects for decision trees and neural nets in protein secondary structure prediction. In BIOKDD01: Workshop on Data mining in Bioinformatics (SIGKDD01), 2001.
Thomas G. Dietterich. An experimental comparison of three methods for costructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, (40):139–157, 2000.
U. M. Fayyad, G. Piatesky-Shapiro, and P. Smith. From data mining to knowledge discovery: an overview. In U. M. Fayyad & al. (Eds), editor, Advances in Knowledge Discovery and Data Mining, pages 1–34. AAAI/MIT Press, 1996.
G. Folino, C. Pizzuti, and G. Spezzano. A cellular genetic programming approach to classification. In Proc. Of the Genetic and Evolutionary Computation Conference GECCO99, pages 1015–1020, Orlando, Florida, July 1999. Morgan Kaufmann.
G. Folino, C. Pizzuti, and G. Spezzano. Genetic programming and simulated annealing: A hybrid method to evolve decision trees. In Riccardo Poli, Wolfgang Banzhaf, William B. Langdon, Julian Miller, Peter Nordin, and Terence C. Fogarty, editors, Proceedings of EuroGP’2000, volume 1802 of LNCS, pages 294–303, Edinburgh, UK, 15–16 April 2000. Springer-Verlag.
G. Folino, C. Pizzuti, and G. Spezzano. Cage: A tool for parallel genetic programming applications. In Julian F. Miller, Marco Tomassini, Pier Luca Lanzi, Conor Ryan, Andrea G. B. Tettamanzi, and William B. Langdon, editors, Proceedings of EuroGP’2001, volume 2038 of LNCS, pages 64–73, Lake Como, Italy, 18–20 April 2001. Springer-Verlag.
G. Folino, C. Pizzuti, and G. Spezzano. Parallel genetic programming for decision tree induction. In Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence ICTAI01, pages 129–135. IEEE Computer Society, 2001.
A. A. Freitas. A genetic programming framework for two data mining tasks: Classification and generalised rule induction. In Proceedings of the 2nd Int. Conference on Genetic Programming, pages 96–101. Stanford University, CA, USA, 1997.
Y. Freund and R. Scapire. Experiments with a new boosting algorithm. In Proceedings of the 13th Int. Conference on Machine Learning, pages 148–156, 1996.
Hitoshi Iba. Bagging, boosting, and bloating in genetic programming. In Proc. Of the Genetic and Evolutionary Computation Conference GECCO99, pages 1053–1060, Orlando, Florida, July 1999. Morgan Kaufmann.
J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge, MA, 1992.
R. E. Marmelstein and G. B. Lamont. Pattern classification using a hybbrid genetic program-decision tree approach. In Proceedings of the Third Annual Conference on Genetic Programming, Morgan Kaufmann, 1998.
C. J. Merz and P. M. Murphy. In UCI repository of Machine Learning, http://www.ics.uci/mlearn/MLRepository.html, 1996.
N. I. Nikolaev and V. Slavov. Inductive genetic programming with decision trees. In Proceedings of the 9th International Conference on Machine Learning, Prague, Czech Republic, 1997.
J. Ross Quinlan. C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, Calif., 1993.
J. Ross Quinlan. Bagging, boosting, and c4.5. In Proceedings of the 13th National Conference on Artificial Intelligence AAAI96, pages 725–730. Mit Press, 1996.
M. D. Ryan and V. J. Rayward-Smith. The evolution of decision trees. In Proceedings of the Third Annual Conference on Genetic Programming, Morgan Kaufmann, 1998.
M. Tomassini. Parallel and distributed evolutionary algorithms: A review. In P. Neittaanmki K. Miettinen, M. Mkel and J. Periaux, editors, Evolutionary Algorithms in Engineering and Computer Science, J. Wiley and Sons, Chichester, 1999.
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Folino, G., Pizzuti, C., Spezzano, G. (2003). Ensemble Techniques for Parallel Genetic Programming Based Classifiers. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_6
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