Outlier detection has attracted more and more attentions from researchers due to its extensive applications in various fields. In this paper, a new SVM (Support Vector Machine) model is presented for a special type of outlier detection problem, where the majority of training data is normal sample and it also contains a small number of anomaly samples. The experimental results on several datasets show that the proposed algorithm is valid and can achieve better accuracy compared with the published algorithms.
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