Support vector machine based on half-suppressed fuzzy c-means clustering
Qiu-Huan Zhao, Minghu Ha, Gui-Bing Peng, Xiankun Zhang · 2009
When dealing with large data sets, the traditional support vector machine (SVM) needs long training time which is aroused by the complexity of computation for kernel function. Moreover, if there are noises in a given training set, the classification accuracy rate of the traditional SVM is usually low. To overcome the shortcomings above, the algorithm of SVM based on half-suppressed fuzzy c-means clustering (HSFCM) is proposed. There are two phases in the proposed algorithm. First, the samples in each of the two classes are clustered by HSFCM. Second, the traditional SVM is trained only by the cluster centers obtained in the first phase. Experimental results show that the proposed method can reduce the number of training samples, enhance the training speed and classification accuracy rate of the traditional SVM effectively.