As for the adversarial data samples which indeed exist in real-world datasets，on the one hand，they can mislead data classifiers into correct predictions which results in poor classification. On the other hand，appropriate applications of the adversarial data samples can distinctly improve the generalization of data classifiers. However，most of existing classification methods do not take the adversarial data samples into account to build corresponding classification models. An adversarial classification algorithm （ACA） based on attacks on the labels of data samples which aims to obtain outperformed classification performance by learning the adversarial data samples is proposed. In a given dataset，a certain percentage of data samples are chosen as adversarial data samples，namely the labels of these chosen data samples are substituted by the other labels which are different from the original labels of the chosen data samples. A SVM model can be generated by using the support vector machine（SVM） algorithm to training the given dataset which contains the adversarial data samples. And the first-order gradient information on the output error of the generated SVM with respect to the input samples can be computed. The input samples can be updated by embedding the first-order gradient information into the original input samples. Consequently，adversarial SVM （A-SVM） can be generated by using the SVM alg-orithm again to train the updated input samples. In terms of theoretical analysis and experimental results on UCI real-world datasets，the mathematically computed first-order gradient information not only provided a positive relation between the outputs and the inputs of a classifier，but also indeed can improve the actual classification performance of A-SVM.