GA-SVM classifying method applied to dynamic evaluation of taekwondo
Ming-Hong Zhong, Jui‐Chung Hung, Yi-Chin Yang, Chih‐Peng Huang · 2016
This paper proposes a GA-SVM classification method which is applied to the dynamic evaluation of taekwondo. For classifying a dynamic action, we converted a dynamic action signal to a frequency spectrum signal for analysis. However, the useful features were concentrated in a part of the frequency spectrum, and the useless features led to a decline in accuracy, operation speed, and efficiency of the classifier. Therefore, we propose a classification method that involves using a support vector machine (SVM) with a genetic algorithm (GA) for the dynamic evaluation of taekwondo. The GA determines the useful features, and the useless features are eliminated, thus reducing the dimensionality and improving the accuracy of the classifier. The SVM can solve problems of small, nonlinear, and high-dimension samples efficiently, exhibiting superior performance in the classification of a dynamic action.