A two-stage support vector machine algorithm based on meta learning and stacking generalization

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.