Explaining outliers occurrence and mechanism of their occurrence can be
extremely important in a variety of domains. Malfunctions, frauds, threats, in
addition to being correctly identified, oftentimes need a valid explanation in
order to effectively perform actionable counteracts. The ever more widespread
use of sophisticated Machine Learning approach to identify anomalies make such
explanations more challenging. We present the Decision Tree Outlier Regressor
(DTOR), a technique for producing rule-based explanations for individual data
points by estimating anomaly scores generated by an anomaly detection model.
This is accomplished by first applying a Decision Tree Regressor, which
computes the estimation score, and then extracting the relative path associated
with the data point score. Our results demonstrate the robustness of DTOR even
in datasets with a large number of features. Additionally, in contrast to other
rule-based approaches, the generated rules are consistently satisfied by the
points to be explained. Furthermore, our evaluation metrics indicate comparable
performance to Anchors in outlier explanation tasks, with reduced execution
time.

通过估算异常检测模型产生的异常分数，我们提出了决策树离群值回归器（DTOR），这是一种用于为单个数据点生成基于规则的解释的技术。我们的结果表明，DTOR 在具有大量特征的数据集中表现出良好的鲁棒性，并且与其他基于规则的方法相比，生成的规则一致满足要解释的数据点。此外，我们的评估指标表明，在异常解释任务中，DTOR 的性能与 Anchors 相当，且执行时间较短。