A boundary method for outlier detection based on support vector domain description

Authors:
S. M. Guo;L. C. Chen;J. S. H. Tsai
Affiliations:
Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, Republic of China;Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, Republic of China;Control System Laboratory, Department of Electrical Engineering, National Cheng-Kung University, Tainan 701, Taiwan, Republic of China
Venue:
Pattern Recognition
Year:
2009

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Abstract

The support vector domain description (SVDD) is a popular kernel method for outlier detection, which tries to fit a class of data with a sphere and uses a few target objects to support its decision boundary. The problem is that even with a flexible Gaussian kernel function, the SVDD could sometimes generate such a loose decision boundary that the discrimination ability becomes poor. Therefore, a computationally intensive procedure called kernel whitening is often required to improve the performance. In this paper, we propose a simple post-processing method which tries to modify the SVDD boundary in order to achieve a tight data description with no need of kernel whitening. With the derivation of the distance between an object and its nearest boundary point in input space, the proposed method can efficiently construct a new decision boundary based on the SVDD boundary. The improvement from the proposed method is demonstrated with synthetic and real-world datasets. The results show that the proposed decision boundary can fit the shape of synthetic data distribution closely and achieves better or comparable classification performance on real-world datasets.