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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org