Abstract
Support vector machines (SVMs) are a well-established classifier, already applied in a variety of pattern recognition tasks. However, they suffer from several drawbacks—selecting their appropriate hyper-parameter values (the SVM model) along with the training sets being the most important. In this paper, we study the influence of applying various kernel functions in SVMs. We verify not only the classification performance of the classifier, but also the number of selected support vectors and the training time for each kernel. Also, we perform the qualitative analysis of the retrieved support vectors using an artificially generated dataset. Finally, we show how to optimize the SVM models using a genetic algorithm. An extensive experimental study revealed that evolved SVM models provide high-quality classification and are retrieved in much shorter time compared with the trial-and-error approaches.
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Notes
- 1.
They are available at sun.aei.polsl.pl/~jnalepa/SVM.
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Acknowledgements
This research was supported by the Polish National Centre for Research and Development under the Innomed Research and Development Grant No. POIR.01.02.00-00-0030/15, and by the Institute of Informatics (Silesian University of Technology) research grant no. BKM-507/RAU2/2016.
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Nalepa, J., Kawulok, M., Dudzik, W. (2018). Tuning and Evolving Support Vector Machine Models. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_41
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