Classifying large and real-world datasets is a challenging problem in machine learning algorithms. Among the machine learning methods, the support vector machine (SVM) is a well-known approach with high generalization ability. Unfortunately, while the number of training data increases and the data contain noise, the performance of SVM signicantly decreases. In this paper, a fast and de-noise two-stage method for training SVMs to deal with large, real- world datasets is proposed. In the rst stage, data that contain noises or are suspected to be noisy are identied and eliminated from the genuine training dataset. The process of elimination and identication is based on the movement of the center of the convex hull data in the training dataset. The convex hull data are computed via the QHull algorithm. On the other hand, the well-known fuzzy clustering method (FCM) is applied to compress and reduce the size of the training dataset. Finally, the reduced and puried cluster centers are used for training the SVM. A set of experiments is conducted on the four benchmarking datasets of the UCI database. Moreover, the amount of training time and the generalization of the proposed approach are compared with FCM-SVM and normal SVM. The results indicate that the proposed method reduces the amount of training time and has a considerable success in removing noisy data from the training dataset. Therefore, the proposed method can achieve a higher generalization performance in comparison with the other methods in large, real-world datasets.

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