带有特征敏感性的扩展隔离森林
Extended Isolation Forest with feature sensitivities
AI总结：
本文提出各向异性隔离森林（AIF），通过调整特征空间中不同方向的敏感性实现更精确的异常检测。
摘要：
与假设数据所有特征的偏差敏感性相等的理论框架相比，允许在不同特征上具有可变敏感性的异常检测理论发展得较少。据我们所知，在基于隔离的方法背景下，这一问题尚未得到解决，本文首次尝试进行探讨。本文引入了带有特征敏感性的扩展隔离森林，我们称之为各向异性隔离森林（AIF）。与标准的EIF不同，AIF能够通过控制不同特征或特征空间中的方向的偏差敏感性来进行异常检测。本文还引入了新的方向敏感性度量方法，这些方法允许量化AIF在特征空间不同方向上的敏感性。这些度量方法使AIF能够根据任务特定的要求调整其敏感性。我们通过将其应用于合成和现实世界数据集来展示该算法的性能。结果表明，AIF能够检测特征空间中偏离典型行为更为重要的方向的异常。
英文摘要：
Compared to theoretical frameworks that assume equal sensitivity to deviations in all features of data, the theory of anomaly detection allowing for variable sensitivity across features is less developed. To the best of our knowledge, this issue has not yet been addressed in the context of isolation-based methods, and this paper represents the first attempt to do so. This paper introduces an Extended Isolation Forest with feature sensitivities, which we refer to as the Anisotropic Isolation Forest (AIF). In contrast to the standard EIF, the AIF enables anomaly detection with controllable sensitivity to deviations in different features or directions in the feature space. The paper also introduces novel measures of directional sensitivity, which allow quantification of AIF's sensitivity in different directions in the feature space. These measures enable adjustment of the AIF's sensitivity to task-specific requirements. We demonstrate the performance of the algorithm by applying it to synthetic and real-world datasets. The results show that the AIF enables anomaly detection that focuses on directions in the feature space where deviations from typical behavior are more important.
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