An Adaptive Weighted One-Class SVM for Robust Outlier Detection

This paper focuses on outlier detection from the perspective of classification. One-class support vector machine (OCSVM) is a widely applied and effective method of outlier detection. Unfortunately experiments show that the standard one-class SVM is easy to be influenced by the outliers contained in the training dataset. To cope with this problem, a robust OCSVM is presented in the paper. In consideration that the contribution yielded by the outlying instances and the normal data is different, a robust one-class SVM which assigns an adapting weight for every object in the training dataset was proposed in this paper. Experimental analysis shows the better performances of the proposed weighted method compared to the conventional one-class SVM on robustness.