改进了传统异常检测方法，能发现特征间依赖关系
Extended Histogram-based Outlier Score (EHBOS)
- 用二维直方图捕捉特征对之间的依赖关系
- 在17个数据集上显著提升异常检测效果，部分场景AUC提升明显
- 适合处理特征交互关键的复杂异常检测任务
基于直方图的异常评分（HBOS）是一种广泛使用的异常检测方法，以计算高效和简单著称。然而，其假设特征独立性限制了在特征交互至关重要的数据集中检测异常的能力。本文提出扩展的基于直方图的异常评分（EHBOS），通过引入二维直方图来捕获特征对之间的依赖关系。该扩展使EHBOS能够识别出HBOS无法检测到的上下文型和依赖驱动型异常。我们在17个基准数据集上评估了EHBOS，证明其在多种异常检测场景中均表现有效且稳健。EHBOS在多个数据集上优于HBOS，尤其在特征交互对异常结构起关键作用的数据集中，取得了显著的ROC AUC提升。结果表明，EHBOS可作为HBOS的有效扩展，具备建模复杂特征依赖的能力。EHBOS为异常检测提供了一个强大新工具，特别适用于上下文或关联异常起重要作用的数据集。
原文摘要 · Abstract (English)
Histogram-Based Outlier Score (HBOS) is a widely used outlier or anomaly detection method known for its computational efficiency and simplicity. However, its assumption of feature independence limits its ability to detect anomalies in datasets where interactions between features are critical. In this paper, we propose the Extended Histogram-Based Outlier Score (EHBOS), which enhances HBOS by incorporating two-dimensional histograms to capture dependencies between feature pairs. This extension allows EHBOS to identify contextual and dependency-driven anomalies that HBOS fails to detect. We evaluate EHBOS on 17 benchmark datasets, demonstrating its effectiveness and robustness across diverse anomaly detection scenarios. EHBOS outperforms HBOS on several datasets, particularly those where feature interactions are critical in defining the anomaly structure, achieving notable improvements in ROC AUC. These results highlight that EHBOS can be a valuable extension to HBOS, with the ability to model complex feature dependencies. EHBOS offers a powerful new tool for anomaly detection, particularly in datasets where contextual or relational anomalies play a significant role.
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