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Multi-class Bayesian support vector data description with anomalies

Author

Listed:
  • Mehmet Turkoz

    (William Paterson University)

  • Sangahn Kim

    (Siena College)

Abstract

Support vector data description (SVDD) procedure fits a spherically shaped boundary around the normal data by minimizing the volume of the description. However, the SVDD may not find an efficient boundary if the normal data consist of multiple classes. In addition to the multi-class normal data, some anomaly observations can be available. We propose a generalized SVDD procedure which finds multiple spheres around the multi-class data by incorporating the anomaly observations into the training procedure. Thus, descriptions for each class include as many as their corresponding class observations by keeping the other class and anomaly observations as far as possible. Moreover, we introduce a generalized Bayesian framework which utilizes the relationships among the classes by not only considering the prior information from normal classes but also the anomaly class. Experiments with various simulation studies and real-life applications demonstrate that the proposed approach can effectively identify the anomalies in multi-class data.

Suggested Citation

  • Mehmet Turkoz & Sangahn Kim, 2022. "Multi-class Bayesian support vector data description with anomalies," Annals of Operations Research, Springer, vol. 317(1), pages 287-312, October.
    • Handle: RePEc:spr:annopr:v:317:y:2022:i:1:d:10.1007_s10479-021-04364-x
    DOI: 10.1007/s10479-021-04364-x
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    References listed on IDEAS

    as
    1. Xianghui Ning & Fugee Tsung, 2013. "Improved design of kernel distance–based charts using support vector methods," IISE Transactions, Taylor & Francis Journals, vol. 45(4), pages 464-476.
    2. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
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