Abstract
Support Vector Machines provide a well established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set. Feature selection and SVM parameters optimization methods improve classification accuracy. This paper studies their joint optimization and attribution improvement. A comparison was made using genetic algorithms to find the best parameters for SVM classification. Results show that using the RBF kernel returns better results on average, though the best optimization for some data sets is highly dependent on the choice of parameters and kernels. We also show that, overall, an average 26% relative improvement with 8% std was obtained.
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Gaspar, P., Carbonell, J., Oliveira, J.L. (2012). Parameter Influence in Genetic Algorithm Optimization of Support Vector Machines. In: Rocha, M., Luscombe, N., Fdez-Riverola, F., Rodríguez, J. (eds) 6th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent and Soft Computing, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28839-5_5
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DOI: https://doi.org/10.1007/978-3-642-28839-5_5
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