Ma Yuliang, Ding Xiaohui, She Qingshan, Luo Zhizeng, Potter Thomas, Zhang Yingchun
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
Comput Math Methods Med. 2016;2016:4941235. doi: 10.1155/2016/4941235. Epub 2016 May 30.
Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.
支持向量机是用于解决小样本和非线性分类问题的强大工具，但其最终分类性能在很大程度上取决于合适的核参数和惩罚参数的选择。在本研究中，我们提出使用粒子群优化算法来优化核参数和惩罚参数的选择，以提高支持向量机的分类性能。通过运动想象脑电信号在分类和预测方面对优化后的分类器性能进行了评估。结果表明，优化后的分类器能显著提高运动想象脑电信号的分类准确率。
