This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (EIF). EIF has shown some interest compared to IF being for instance more robust to some artefacts. However, some information can be lost when computing the EIF trees since the sampled threshold might lead to empty branches. This letter introduces a generalized isolation forest algorithm called Generalized IF (GIF) to overcome these issues. GIF is faster than EIF with a similar performance, as shown in several simulation results associated with reference databases used for anomaly detection.

中文翻译：

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用于异常检测的广义隔离森林

这封信介绍了基于现有扩展 IF (EIF) 的隔离森林 (IF) 的泛化。与 IF 相比，EIF 表现出了一些兴趣，例如对某些人工制品更稳健。但是，在计算 EIF 树时可能会丢失一些信息，因为采样阈值可能会导致空分支。这封信介绍了一种称为 Generalized IF (GIF) 的广义隔离森林算法来克服这些问题。GIF 比 EIF 快，但性能相似，如与用于异常检测的参考数据库相关的几个模拟结果所示。