Multiclass problems with binary SVM classifiers are commonly treated as a decomposition in several binary sub-problems. An open question is how to properly tune all these sub-problems (SVM hyperparameters) in order to have the lowest error rate for a SVM multiclass scheme based on decomposition. In this paper, we propose a new approach to optimize the generalization capacity of such SVM multiclass schemes. This approach consists in a global selection of hyperparameters for sub-problems all together and it is denoted as multi-model selection. A multi-model selection can outperform the classical individual model selection used until now in the literature, An evolutionary algorithm (EA) is proposed to perform multi-model selection. Experimentations with our EA method show the benefits of our approach over the classical one. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Lebrun, G., Lezoray, O., Charrier, C., & Cardot, H. (2007). An EA multi-model selection for SVM multiclass schemes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 260–267). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_32
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