Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm
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The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs; in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm.
KeywordsOptimization algorithms Support vector machine (SVM) Parameter optimization Imbalanced data
Compliance with ethical standards
Conflict of Interest
The authors declare that they have no conflict of interest.
- 3.Wang Y, Wang Y, Tan T (2004) Combining fingerprint and voiceprint biometrics for identity verification: an experimental comparison. In: Biometric authentication, pp 289–294Google Scholar
- 8.Yamany W, Tharwat A, Hassanin M F, Gaber T, Hassanien AE, Kim TH (2015) A new multi-layer perceptrons trainer based on ant lion optimization algorithm. In: Fourth international conference on information science and industrial applications (ISI). IEEE, pp 40–45Google Scholar
- 19.Tharwat A, Gabel T, Hassanien AE (2017) Parameter optimization of support vector machine using dragonfly algorithm. In: International conference on advanced intelligent systems and informatics. Springer, pp 309–319Google Scholar
- 24.Tharwat A (2019) Parameter investigation of support vector machine classifier with kernel functions. Knowl Inf Syst 1–34. https://doi.org/10.1007/s10115-019-01335-4
- 29.Tharwat A (2018) Classification assessment methods. Appl Comput Inform. https://doi.org/10.1016/j.aci.2018.08.003
- 33.Tharwat A, Gaber T, Hassanien AE, Elnaghi BE (2017) Particle swarm optimization: a tutorial. In: Handbook of research on machine learning innovations and trends. IGI Global, pp 614–635Google Scholar