Optimal asymmetrical SVM using pattern search. A health care application.

This paper considers the model selection problem for Support Vector Machines. A well-known derivative Pattern Search method, which aims to tune hyperparameter values using an empirical error estimate as a steering criterion, is proposed. This approach is experimentally evaluated on a health care problem which involves discriminating nosocomially infected patients from non-infected patients. The Hooke and Jeeves Pattern Search (HJPS) method is shown to improve the results achieved by Grid Search (GS) in terms of solution quality and computational efficiency. Unlike most other parameter tuning techniques, our 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 good performance and convergence properties.