Multi-Objective Optimization for SVM Model Selection

Clément Chatelain (1) , Sebastien Adam (1) , Yves Lecourtier (1) , Laurent Heutte (1) , Thierry Paquet (1)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

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).

Domaines: Traitement du texte et du document

Clément Chatelain, Sebastien Adam, Yves Lecourtier, Laurent Heutte, Thierry Paquet. Multi-Objective Optimization for SVM Model Selection. ICDAR, Sep 2007, Curitiba, Brazil. pp.427-431. ⟨hal-00435961⟩