Abstract
Modeling of processes with many input variables requires selection of informative inputs in order to construct less complex models with good generalization abilities. In this paper two feature selection methods are compared: mutual information (MI) based feature selection and genetic algorithm (GA) based feature selection. As a modeling structure a hybrid linear-neural model is used. The methods are applied to a case study: modeling of an industrial antibiotic fermentation process. It is shown that both feature selection methods can lead to similar results. 8s based feature selection can be applied to problems where only few data exist and MI can not be calculated. In GA based feature selection it is possibile to adjust the objective function in order to control the propperties of the method.
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© 1999 Springer-Verlag Wien
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Potočnik, P., Grabec, I. (1999). Selection of Informative Inputs Using Genetic Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_45
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_45
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
eBook Packages: Springer Book Archive