SVM-OD: SVM method to detect outliers

Publication Type:
Conference Proceeding
Studies in Computational Intelligence, 2006, 9 pp. 129 - 141
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Outlier detection is an important task in data mining because outliers can be either useful knowledge or noise. Many statistical methods have been applied to detect outliers, but they usually assume a given distribution of data and it is difficult to deal with high dimensional data. The Statistical Learning Theory (SLT) established by Vapnik et al. provides a new way to overcome these drawbacks. According to SLT Schölkopf et al. proposed a ν-Support Vector Machine (ν-SVM) andapplied it to detect outliers. However, it is still difficult for data mining users to decide onekey parameter in ν-SVM. This paper proposes a new SVM method to detect outliers, SVM-OD, which can avoid this parameter. We provide the theoretical analysis based on SLT as well as experiments to verify the effectiveness of our method. Moreover, an experiment on synthetic data shows that SVM-OD can detect some local outliers near the cluster with some distribution while ν-SVM cannot dothat. © 2006 Springer-Verlag Berlin/Heidelberg.
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