基于元学习和叠加法的双层支持向量机算法

A Two-Stage Support Vector Machine Algorithm Based on Meta Learning and Stacking Generalization

提出一种模式识别算法——双层支持量机算法,用来提高表面肌电识别精度.该算法融合集成学习中元学习的并行方法和叠加法的递进思想,把基本SVM分类器并行分布在第1层,第1层的预测结果作为第2层的输入,由第2层再进行分类识别,从而通过多层分类器组合来融合多源特征.以手臂表面肌电数据集为测试数据,采用文中的双层支持向量机,各肌肉的肌电信号分别输入基支持向量机,组合器融合各肌肉电信号特征,集成识别前臂肌肉群的肌电信号,从而实现运动意图的精确识别.实验结果显示,在预测精度上,此算法优于单个SVM分类器.在预测性能上(识别精度、耗时、鲁棒性),此算法优于随机森林和旋转森林等集成分类器.

A Two-Stage Support Vector Machine Algorithm （TSSVM） is proposed to improve the recognition accuracy of the surface electromyography （SEMG）. The proposed algorithm is integrated with parallel method of meta-learning and the stacking idea of ensemble learning. In this algorithm, the basic classifiers are paralleled and distributed on the first stage and the outputs of the first-stage Support Vector Machine （SVM） are input into the second-stage SVM to integrate multi-source features and output the classification result. And then the proposed algorithm is used on test data set of the SEMG from human upper limb. The signals of SEMGs from individual muscles are respectively input into the first-stage SVMs. And the output of the first-stage SVMs is input into the second-stage SVM combiner to integrate and recognize the electromyographic signal features of individual muscle. Results show that TSSVM is superior to single SVM in classification accuracy. Moreover, TSSVM outperforms other state-of-art ensemble classifiers, such as random forest and rotation forest in classification accuracy, time cost and robustness.