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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© 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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