Anomaly detection consists of detecting elements of a database that are different from the majority of normal data. The majority of anomaly detection algorithms considers unlabeled datasets. However, in some applications, labels associated with a subset of the database (coming for instance from expert feedback) are available providing useful information to design the anomaly detector. This paper studies a semi-supervised anomaly detector based on support vector machines, which takes the best of existing supervised and unsupervised support vector machines algorithms. The proposed algorithm allows the maximum proportion of vectors detected as anomalies and the maximum proportion of errors in the supervised data to be controlled, through two hyperparameters defining these proportions. Simulations conducted on various benchmark datasets show the interest of the proposed semi-supervised anomaly detection method.

异常检测包括检测与大多数正常数据不同的数据库元素。大多数异常检测算法都考虑未标记的数据集。但是，在某些应用程序中，与数据库子集（例如来自专家反馈）相关联的标签可提供有用的信息来设计异常检测器。本文研究了一种基于支持向量机的半监督异常检测器，它充分利用了现有的有监督和无监督支持向量机算法。所提出的算法允许通过定义这些比例的两个超参数来控制被检测为异常的向量的最大比例和监督数据中的最大错误比例。