Abstract
Support vector machine (SVM) is a popular classifier that has been used to solve a broad range of problems. Unfortunately, its applications are limited by computational complexity of training which is \(O(t^3)\), where t is the number of vectors in the training set. This limitation makes it difficult to find a proper model, especially for non-linear SVMs, where optimization of hyperparameters is needed. Nowadays, when datasets are getting bigger in terms of their size and the number of features, this issue is becoming a relevant limitation. Furthermore, with a growing number of features, there is often a problem that a lot of them may be redundant and noisy which brings down the performance of a classifier. In this paper, we address both of these issues by combining a recursive feature elimination algorithm with our evolutionary method for model and training set selection. With all of these steps, we reduce both the training and classification times of a trained classifier. We also show that the model obtained using this procedure has similar performance to that determined with other algorithms, including grid search. The results are presented over a set of well-known benchmark sets.
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Acknowledgement
This work was supported by the National Science Centre under Grant DEC-2017/25/B/ST6/00474, and by the Silesian University of Technology, Poland, funds no. BKM-509/RAu2/2017.
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Dudzik, W., Nalepa, J., Kawulok, M. (2019). Automated Optimization of Non-linear Support Vector Machines for Binary Classification. In: Xhafa, F., Barolli, L., Greguš, M. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-98557-2_47
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DOI: https://doi.org/10.1007/978-3-319-98557-2_47
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