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SVDD-based outlier detection on uncertain data

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Abstract

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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Authors and Affiliations

  1. Faculty of Automation, Guangdong University of Technology, Guangdong, People’s Republic of China

    Bo Liu

  2. Faculty of Computer, Guangdong University of Technology, Guangdong, People’s Republic of China

    Yanshan Xiao & Zhifeng Hao

  3. Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

    Longbing Cao

  4. School of Automation Science and Engineering, South China University of Technology, Guangdong, People’s Republic of China

    Feiqi Deng

Authors
  1. Bo Liu
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  2. Yanshan Xiao
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  3. Longbing Cao
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  4. Zhifeng Hao
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  5. Feiqi Deng
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Correspondence to Zhifeng Hao.

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Liu, B., Xiao, Y., Cao, L. et al. SVDD-based outlier detection on uncertain data. Knowl Inf Syst 34, 597–618 (2013). https://doi.org/10.1007/s10115-012-0484-y

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  • DOI: https://doi.org/10.1007/s10115-012-0484-y

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