Abstract
A method for the data mining task of data classification, suitable to be implemented on massively parallel architectures, is proposed. The method combines genetic programming and simulated annealing to evolve a population of decision trees. A cellular automaton is used to realise a fine-grained parallel implementation of genetic programming through the diffusion model and the annealing schedule to decide the acceptance of a new solution. Preliminary experimental results, obtained by simulating the behaviour of the cellular automaton on a sequential machine, show significant better performances with respect to C4.5.
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References
Chen, M., Han, J., Yu, P.S.: Data Mining: an Overview from Database Perspective. IEEE Transaction on Knowledge and Data Engineering 8(6), 866–883 (1996)
Chen, H., Flann, N.S., Watson, D.W.: Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm. IEEE Transaction on Parallel and Distributed Systems 9(2)
Fayyad, U.M., Piatesky-Shapiro, G., Smith, P.: From Data Mining to Knowledge Discovery: an overview. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI/MIT Press (1996)
Folino, G., Pizzuti, C., Spezzano, G.: A Cellular Genetic Programming Approach to Classification. In: Proc. of the Genetic and Evolutionary Computation Conference GECCO 1999, Orlando, Florida, pp. 1015–1020. Morgan Kauffmann, San Fransisco (1999)
Freitas, A.A.: A Genetic Programming Framework for two Data Mining Tasks: Classification and Generalised Rule Induction. In: GP 1997: Proc. 2nd Annual Conference, Stanford University, CA, USA, pp. 96–101 (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Welsey, Reading (1989)
Kirkpatrick, S., Gellant, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Lin, F.T., Kao, C.Y., Hsu, C.C.: Incorporating Genetic Algorithms into Simulated Annealing. In: Proc. Fourth Int. Symposium Artificial Intelligence, pp. 290–297 (1991)
Koza, J.R.: Genetic Programming: On Programming Computers by Means of Natural Selection and Genetics. MIT Press, Cambridge (1992)
Koza, J.R., Andre, D.: Parallel genetic programming on a network of transputers. Technical Report CS-TR-95-1542, Computer Science Department, Stanford University (1995)
Nikolaev, N.I., Slavov, V.: Inductive Genetic Programming with Decision Trees. In: Proceedings of the 9th International Conference on Machine Learning, Prague, Czech Republic (April 1997)
Marmelstein, R.E., Lamont, G.B.: Pattern Classification using a Hybrid Genetic Program - Decision Tree approach. In: Proceedings of the Third Annual Conference on Genetic Programming. Morgan Kaufmann, San Francisco (1998)
Martin, W.N., Lienig, J., Cohoon, J.P.: Island (migration) models: evolutionary algorithms based on punctuated equilibria. In: Back, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of evolutionary Computation. IOP Publishing and Oxford University Press (1997)
Merz, C.J., Murphy, P.M.: UCI repository of Machine Learning (1996), http://www.ics.uci/mlearn/MLRepository.html
Pettey, C.C.: Diffusion (cellular) models. In: Back, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of evolutionary Computation. IOP Publishing and Oxford University Press (1997)
Piatesky-Shapiro, G., Frawley, W.J.: Knowledge Discovery in Databases. AAAI/MIT Press (1991)
Ross Quinlan, J.: C4-5 Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Ryan, M.D., Rayward-Smith, V.J.: The Evolution of Decision Trees. In: Proceedings of the Third Annual Conference on Genetic Programming. Morgan Kaufmann, San Francisco (1998)
Tackett, W.A.: Genetic Programming for feature discovery and image discrimination. In: Proceedings of the Fifth International Conference on Genetic Algorithms (1993)
Tackett, W.A., Carmi, A.: Simple Genetic Programming in C, Available through the genetic programming archive at ftp://ftp.io.com/pub/genetic-programming/code/sgpcl.tar.Z
Toffoli, T., Margolus, N.: Cellular Automata Machines. A New Environment for Modeling. The MIT Press, Cambridge (1986)
Whitley, D.: Cellular Genetic Algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco (1993)
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Folino, G., Pizzuti, C., Spezzano, G. (2000). Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_22
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DOI: https://doi.org/10.1007/978-3-540-46239-2_22
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