HBS and HFS feature selection methods for Chinese folk music classification
Hui Song, Ke Sun, Baiyan Li, Xiaoqiang Liu · 2011
In this paper, we perform an exploring on music characteristics of Chinese folk music, in order to do targeted music restoration and perform digital reproduction on music segments. Starting from the point of music classification and music feature selection, we firstly choose SVM as the classifier according to experiment results of different classifiers. Then we introduce two common filter-filter methods: ReliefF-PCA and ReliefF-CA, and put forward to two heuristic filter-wrapper methods: HBS and HFS. At last, we apply these feature selection methods on our music data set and test their performance through experiments. The results show that HBS and HFS algorithms can effectively reduce the dimension of features vector and improve the classification accuracy, while other common feature selection methods perform poorer than them.