SVM-OD: SVM Method to Detect Outliers
- Publisher:
- Springer
- Publication Type:
- Journal Article
- Citation:
- Studies in Computational Intelligence, 2005, 9 pp. 129 - 141
- Issue Date:
- 2005-01
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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 Scholkopf et al. proposed a v-Support Vector Machine (v-SVM) and applied it to detect outliers. However, it is still difficult for data mining users to decide one key parameter in v-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 v-SVM cannot do that
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