Multi-sphere Support Vector Data for Outliers Detection on Multi-distribution Data

IEEE Computer Society Press
Publication Type:
Conference Proceeding
Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on, 2009, pp. 82 - 87
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2009001811OK.pdf3.74 MBAdobe PDF
SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detection method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD.
Please use this identifier to cite or link to this item: