Algorithm

Process (computing)

Bayesian probability

Support vector machine

Operating system

Anomaly detection

Data mining

Computer science

Anomaly (physics)

Physics

Pattern recognition (psychology)

Condensed matter physics

Artificial intelligence

Class (philosophy)

Mode (computer interface)

Pattern Recognition

Traditional anomaly detection procedures assume that normal observations are obtained from a single distribution. However, due to the complexities of modern industrial processes, the observations may belong to multiple operating modes with different distributions. In such cases, traditional anomaly detection procedures may trigger false alarms while the process is indeed in another normally operating mode. We propose a generalized support vector-based anomaly detection procedure called generalized support vector data description which can be used to determine the anomalies in multimodal processes. The proposed procedure constructs hyperspheres for each class in order to include as many observations as possible and keep other class observations as far apart as possible. In addition, we introduce a generalized Bayesian framework which does not only consider the prior information from each mode, but also highlights the relationships among the modes. The effectiveness of the proposed procedure is demonstrated through various simulation studies and real-life applications.

Generalized support vector data description for anomaly detection