Abstract
One-class SVM (OCSVM) is widely adopted in one-class classification (OCC) fields. However, outliers in the training set negatively influence the classification surface of OCSVM, degrading its performance. To solve this problem, a novel method is proposed in this paper. This proposed method introduces Ramp Loss function into OCSVM optimization, so as to reduce outliers’ influence. Then the outliers are identified and removed from the training set. The final classification surface is obtained on the remaining training samples. Various experiments verify the effectiveness of this proposed method.
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