论文标题
基于依赖关系的异常检测：一般框架和全面评估
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
论文作者
论文摘要
异常检测对于理解数据中的异常行为至关重要，因为异常提供了宝贵的见解。本文介绍了基于依赖关系的异常检测（DEPAD），该检测是一种通用框架，该框架利用可变依赖项来发现有意义的异常，具有更好的解释性。 DEPAD将无监督的异常检测重新放置为监督功能选择和预测任务，该任务允许用户根据其特定问题和数据量身定制异常检测算法。我们广泛评估了DEPAD框架的代表性现成技术。与九种最先进的异常检测方法相比，两种DePAD算法作为全能者和出色的数据集出现。此外，我们证明DEPAD算法为检测到的异常提供了新的和有见地的解释。
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.
