Effectiveness of Random Search in SVM hyper-parameter tuning

• Autor(a) principal: Mantovani, Rafael G.
• Data de Publicação: 2015
• Outros Autores: Rossi, André L. D. [UNESP], Vanschoren, Joaquin, Bischl, Bernd, De Carvalho, André C.P.L.F.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IJCNN.2015.7280664; http://hdl.handle.net/11449/168228
• topic: Accuracy; Blogs; Computational modeling; Heating; Lead; Support vector machines; Training

Classification is one of the most common machine learning tasks. SVMs have been frequently applied to this task. In general, the values chosen for the hyper-parameters of SVMs affect the performance of their induced predictive models. Several studies use optimization techniques to find a set of hyper-parameter values that induces classifiers with good predictive performance. This paper investigates the hypothesis that a simple Random Search method is sufficient to adjust the hyper-parameters of SVMs. A set of experiments compared the performance of five tuning techniques: three meta-heuristics commonly used, Random Search and Grid Search. The experimental results show that the predictive performance of models using Random Search is equivalent to those obtained using meta-heuristics and Grid Search, but with a lower computational cost.