Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
This work addresses the need for more interpretable and adaptable anomaly detection methods in data analysis, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of anomaly detection by introducing Dependency-based Anomaly Detection (DepAD), a general framework that uses variable dependencies to improve interpretability, and shows that two DepAD algorithms outperform nine state-of-the-art methods across various datasets.
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a general framework that utilizes variable dependencies to uncover meaningful anomalies with better interpretability. DepAD reframes unsupervised anomaly detection as supervised feature selection and prediction tasks, which allows users to tailor anomaly detection algorithms to their specific problems and data. We extensively evaluate representative off-the-shelf techniques for the DepAD framework. Two DepAD algorithms emerge as all-rounders and superior performers in handling a wide range of datasets compared to nine state-of-the-art anomaly detection methods. Additionally, we demonstrate that DepAD algorithms provide new and insightful interpretations for detected anomalies.