title: SVM Modeling via a Hybrid Genetic Strategy. A Health Care Application.
abstract: This paper addresses the model selection problem for Support Vector Machines. A hybrid genetic algorithm guided by Direct Simplex Search to evolves hyperparameter values using an empirical error estimate as a steering criterion. This approach is specificaly tailored and experimentally evaluated on a health care problem which involves discriminating 11 % nosocomially infected patients from 89 % non infected patients. The combination of Direct Search Simplex with GAs is shown to improve the performance of GAs in terms of solution quality and computational efficiency. Unlike most other hyperparameter tuning techniques, our hybrid approach does not require supplementary effort such as computation of derivatives, making them well suited for practical purposes. This method produces encouraging results: it exhibits high performance and good convergence properties.
authors.affiliation: Service of Medical Informatics, University Hospital of Geneva, 1211 Geneva, Switzerland. gilles.cohen@sim.hcuge.ch
authors.forename: Gilles
authors.initials: G
authors.lastname: Cohen
authors.forename: Mélanie
authors.initials: M
authors.lastname: Hilario
authors.forename: Christian
authors.initials: C
authors.lastname: Pellegrini
authors.forename: Antoine
authors.initials: A
authors.lastname: Geissbuhler
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008962:Models, Theoretical; D060388:Support Vector Machine