Abstract
In this paper we present a novel Genetic Algorithm (GA) for feature selection in machine learning problems. We introduce a novel genetic operator which fixes the number of selected features. This operator, we will refer to it as m-features operator, reduces the size of the search space and improves the GA performance and convergence. Simulations on synthetic and real problems have shown very good performance of the m-features operator, improving the performance of other existing approaches over the feature selection problem.
This work has been partially supported by a CICYT grant number: TIC-1999-0216.
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References
Blum, A. and Langley, P.: Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence, 97, (1997) 245–271.
Weston, H., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: In: Sara A Solla, Todd K Leen, and Klaus-Robert Muller, (eds): Feature Selection for SVMs. Advances in NIPS 12, MIT Press, (2000) 526–532.
Kohavi, R., John, G. H.: Wrappers for Features Subset Selection. Int. J. Digit. Libr, 1, (1997) 108–121
Annual KDD Cup: http://www.cs.wisc.edu/~dpage/kddcup2001/.
Vapnik, V. N.: Statistical learning theory. John Wiley & sons, New York, 1998.
Chen, H. Y., Chen, T. C., Min, D. I., Fischer G. W. and Wu, Y. M: Prediction of Tacrolimus Blood Levels by Using the Neural Network with Genetic Algorithm in Liver Transplantation Patients. Therapeutic Drug Monitoring 21, (1999) 50–56.
Vafaie, H and De Jong, K. A.: Genetic Algorithms as a Tool for Features Selection in Machine Learning. Proc. of the 4th int. conference on tools with artificial systems, IEEE computer society press, Arlintong, VA, (1992) 200–204.
Goldberg, D. E: Genetic Algorithm in Search Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc (1989).
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© 2002 Springer-Verlag Berlin Heidelberg
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Salcedo-Sanz, S., Prado-Cumplido, M., Pérez-Cruz, F., Bousoño-Calzón, C. (2002). Feature Selection via Genetic Optimization. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_89
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DOI: https://doi.org/10.1007/3-540-46084-5_89
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