Fuzzy support vector machines based on FCM clustering

Fuzzy support vector machines based on fuzzy c-means clustering are proposed in this paper. They apply the fuzzy c-means clustering technique to each class of the training set. During the clustering with a suitable fuzziness parameter q, the more important samples, such as support vectors, become the cluster centers respectively. All the cluster centers generated by fuzzy c-means clustering are selected as the representations of the other similar samples close to the cluster centers. The new training set consisting of all the centers is used to form fuzzy support vector machines. Experimental results on the benchmark data sets show that the proposed fuzzy support vector machines need less training data and less quadratic programming time compared with the conventional fuzzy support vector machines, and their classification accuracy rates are acceptable.