The computation of the classification process of Twin Support Vector Machines (TSVMs) is proportional to the number of samples. When the number of samples is large, the classification process will be time-consuming. In order to improve the sparsity of sample sets, a Fast Classification algorithm of Twin Support Vector Machines based on Affinity Propagation clustering (FCTSVMs-AP) is proposed. FCTSVMs-AP first performs adaptive AP clustering on the original data set. The center of the cluster is be used as the new sample set after reduction. According to the principle of minimum classification error, an optimization model is constructed. Moreover, the coefficients of the new decision tree is solved by quadratic programming. Furthermore, we prove that, when the sample set is compressed, the error between the new fast decision function and the original decision function is equivalent to the adaptive AP clustering of the original data set in the sample space. Experiments on artificial datasets and UCI datasets show that FCTSVMs-AP can improve the classification speed by effectively compressing the number of samples, while the loss of classification accuracy is not statistically significant.

Key words:
 

 ,