2004 | OriginalPaper | Buchkapitel
Model Selection for Support Vector Classifiers via Genetic Algorithms. An Application to Medical Decision Support
verfasst von : Gilles Cohen, Mélanie Hilario, Antoine Geissbuhler
Erschienen in: Biological and Medical Data Analysis
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This paper addresses the problem of tuning hyperparameters in support vector machine modeling. A Genetic Algorithm-based wrapper, which seeks to evolve hyperparameter values using an empirical error estimate as a fitness function, is proposed and experimentally evaluated on a medical dataset. Model selection is then fully automated. Unlike other hyperparameters tuning techniques, genetic algorithms do not require supplementary information making them well suited for practical purposes. This approach was motivated by an application where the number of parameters to adjust is greater than one. This method produces satisfactory results.