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To solve the problem that traditional over-sampling algorithms may cause the decision-making domain becomes smaller and the noise point increases while sample was being increased, this paper presented a mixed-sampling algorithm based on misclassified samples. This approach used support vector machine be as base classifier and the misclassified samples be identified during each iteration, according to their spatial relationship between neighbors of each misclassified samples, it took an improved mixed-sampling strategy: remove this directly to the noise samples and exclude positive class samples in the neighbors to the dangerous samples, while, to security samples, compose new samples by SMOTE algorithm, then added to the original training set to retrain the classification model. Compared with SMOTE-SVM algorithm and AdaBoost-SVM-OBMS algorithm on real data sets, the experimental results finally show that this algorithm can effectively improve the classification accuracy of the negative class. [ABSTRACT FROM AUTHOR]