Feature Selection via Genetic Optimization

Authors:
Sancho Salcedo-Sanz;Mario Prado-Cumplido;Fernando Pérez-Cruz;Carlos Bousoño-Calzón
Affiliations:
-;-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 2
Cited 3

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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.