Abstract
The use of machine learning techniques to automatically analyze data for information is becoming increasingly widespread. In this chapter we examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified using the C4.5 decision tree learning algorithm. Genetic Programming is used to construct new features from those available in the data, a potentially significant process for data mining since it gives consideration to hidden relationships between features. A Genetic Algorithm is used to determine which set of features is the most predictive. Using ten well-known data sets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases.
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References
Ahluwalia, M., Bull, L. (1999): Co-evolving functions in genetic programming: classification using k-nearest neighbour. In Banzhaf, W., Daida, J., Eiben, G., Garzon, M. H., Honavar, J., Jakeila, K., Smith, R. (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 947–9512
Dixon, P. W., Corne, D. W., Oates, M. J. (2001): A preliminary investigation of modified XCS as a generic data mining tool. In Lanzi, P. L., Stolzmann, W., Wilson, S. (eds) Advances in Learning Classifier Systems. Springer, pp.133–151
Ekárt, A., Márkus, A. (2003): Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms. To appear in Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Holland, J.H. (1975): Adaptation in natural and artificial systems. Univ. Michigan
Kelly, J.D., Davis, L. (1991): Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm. In R. Belew, L. Booker (eds) Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, 377–383
Kohavi, R., John, G. H. (1997): Wrappers for feature subset selection. Artificial Intelligence Journal 1,2, 273–324
Koza, J.R. (1992): Genetic programming. MIT Press
Krawiec, Krzysztof (2002): Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines 3, 329–343
Mangasarian, O. L., Musicant, D. R. (2001): Lagrangian support vector machines. Journal of Machine Learning Research, 1, 161–177
Otero, F. E. B., Silva, M. M. S., Freitas, A. A., Nievola, J. C. (2003): Genetic programming for attribute construction in data mining. In Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (Eds.) Genetic Programming: 6th European Conference, EuroGP 2003, Essex, UK, Proceedings. Springer, 384–393
Quinlan, J.R. (1993): C4.5: Programs for machine learning. Morgan Kaufmann
Raymer, M.L., Punch, W., Goodman, E.D., Kuhn, L. (1996): Genetic programming for improved data mining — application to the biochemistry of protein interactions. In Koza, J. R., Deb, K., Dorigo, M., Fogel, D. B., Garzon, M., Iba, H., Riolo, R. (eds) Proceedings of the Second Annual Conference on Genetic Programming, Morgan Kaufmann, 375–380
Siedlecki, W., Sklansky, J. (1988): On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence 2, 197–220
Vafaie, H., De Jong, K. (1995): Genetic algorithms as a tool for restructuring feature space representations. In Proceedings of the International Conference on Tools with A.I. IEEE Computer Society Press
Witten, I.H., Frank, E. (2000): Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann
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Smith, M.G., Bull, L. (2005). GAP: Constructing and Selecting Features with Evolutionary Computing. In: Ghosh, A., Jain, L.C. (eds) Evolutionary Computation in Data Mining. Studies in Fuzziness and Soft Computing, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32358-9_3
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DOI: https://doi.org/10.1007/3-540-32358-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22370-2
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