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| Outlier One Class Support Vector Machines |
| Tian Jiang; Gu Hong |
| School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116023, China |
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Abstract One-Class Support Vector Machines (OCSVMs) distinguish outliers by computing a hyper-plane in feature space. The choice of the origin as separation point is arbitrary, which affects the decision boundary, and the distribution of samples has impact on the performance. Expanding the algorithm into solving two-class classification problems overcomes the drawbacks to a certain degree. However, the class imbalance problem is serious and the labeled outliers are rare or even non-existing. In this paper, a new “Outlier OCSVM” is proposed and a framework is designed for unsupervised outlier detection. Respectively scored by distance from hyper-plane and probabilistic output value, two definitions of outlier degree are presented. After picking out some suspicious outliers via combining the two criterions of outlier degree, the adjusted “Outlier OCSVM” starts the training operations, two parts of the dataset are updated interactively through comparison of the outputs. Experiment results on benchmark datasets show that the method can effectively improve the detection rate and reduce false positive rate, easy and reliable.
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Received: 09 June 2009
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Corresponding Authors:
Tian Jiang
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2010, Vol. 32 
