期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 34, 期 3, 页码 597-618出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-012-0484-y
关键词
Outlier detection; Data of uncertainty; Support vector data description
资金
- Natural Science Foundation of China [61070033]
- Natural Science Foundation of Guangdong province [9251009001000005, S2011040004187]
- Science and Technology Planning Project of Guangdong Province [2010B050400011, 2010B080701070, 2008B080701005]
- Eleventh Five-year Plan Program of the Philosophy and Social Science of Guangdong Province [08O-01]
- Opening Project of the State Key Laboratory of Information Security [04-01]
- China Post-Doctoral First-Class Fund [L1100010]
Outlier detection is an important problem that has been studied within diverse research areas and application domains. Most existing methods are based on the assumption that an example can be exactly categorized as either a normal class or an outlier. However, in many real-life applications, data are uncertain in nature due to various errors or partial completeness. These data uncertainty make the detection of outliers far more difficult than it is from clearly separable data. The key challenge of handling uncertain data in outlier detection is how to reduce the impact of uncertain data on the learned distinctive classifier. This paper proposes a new SVDD-based approach to detect outliers on uncertain data. The proposed approach operates in two steps. In the first step, a pseudo-training set is generated by assigning a confidence score to each input example, which indicates the likelihood of an example tending normal class. In the second step, the generated confidence score is incorporated into the support vector data description training phase to construct a global distinctive classifier for outlier detection. In this phase, the contribution of the examples with the least confidence score on the construction of the decision boundary has been reduced. The experiments show that the proposed approach outperforms state-of-art outlier detection techniques.
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