Multiclass SVM Model Selection Using Particle Swarm Optimization
Bruno Bezerra de Souza, André de Carvalho, Rodrigo Calvo, Renato Porfírio Ishii · International Conference Hybrid Intelligent Systems · 2006
Tuning SVM hyperparameters is an important step for achieving good classification performance. In the binary case, the model selection issue is well studied. For multiclass problems, it is harder to choose appropriate values for the base binary models of a decomposition scheme. In this paper, the authors employ Particle Swarm Optimization to perform a multiclass model selection, which optimizes the hyperparameters considering both local and globalmodels. Experiments conducted over 4 benchmark problems show promising results.