Multi-Objective Optimization for SVM Model Selection

In this paper, we propose a multi-objective optimization method for SVM model selection using the well known NSGA-II algorithm. FA and FR rates are the two criteria used to find the optimal hyperparameters of a set of SVM classifiers. The proposed strategy is applied to a digit/outlier discrimination task embedded in a more global information extraction system that aims at locating and recognizing numerical fields in handwritten incoming mail documents. Experiments conducted on a large database of digits and outliers show clearly that our method compares favorably with the results obtained by a state-of-the- art mono-objective optimization technique using the classical Area Under ROC Curve criterion (AUC).