Bio-inspired Optimization Techniques for SVM Parameter Tuning

Machine learning techniques have been successfully applied to a large number of classification problems. Among these techniques, support vector machines (SVMs) are well know for the good classification accuracies reported in several studies. However, like many machine learning techniques, the classification performance obtained by SVMs is influenced by the choice of proper values for their free parameters. In this paper, we investigate what is the influence of different optimization techniques inspired by biology when they are used to optimize the free parameters of SVMs. This comparative study also included the default values suggested in the literature for the free parameters and a grid algorithm used for parameter tuning. The results obtained suggest that, although SVMs work well with the default values, they can benefit from the use of an optimization technique for parameter tuning.