Surrogate-assisted multi-objective model selection for support vector machines
Classification is one of the most well-known tasks in supervised learning. A vast number of algorithms for pattern classification have been proposed so far. Among these, support vector machines (SVMs) are one of the most popular approaches, due to the high performance reached by these methods in a wide number of pattern recognition applications. Nevertheless, the effectiveness of SVMs highly depends on their hyper-parameters. Besides the fine-tuning of their hyper-parameters, the way in which the features are scaled as well as the presence of non-relevant features could affect their generalization performance. This paper introduces an approach for addressing model selection for support vector machines used in classification tasks. In our formulation, a model can be composed of feature selection and pre-processing methods besides the SVM classifier. We formulate the model selection problem as a multi-objective one, aiming to minimize simultaneously two components that are closely related to the error of a model: bias and variance components, which are estimated in an experimental fashion. A surrogate-assisted evolutionary multi-objective optimization approach is adopted to explore the hyper-parameters space. We adopted this approach due to the fact that estimating the bias and variance could be computationally expensive. Therefore, by using surrogate-assisted optimization, we expect to reduce the number of solutions evaluated by the fitness functions so that the computational cost would also be reduced. Experimental results conducted on benchmark datasets widely used in the literature, indicate that highly competitive models with a fewer number of fitness function evaluations are obtained by our proposal when it is compared to state of the art model selection methods.
model selection support vector machines multi-objective optimization surrogate-assisted optimization