用随机森林学习数据相似性，提升无监督异常检测精度。
Explainable Unsupervised Anomaly Detection with Random Forest
- 训练随机森林区分真实数据与均匀采样合成数据，生成非对称距离度量
- 在多个基准数据集上优于常见异常检测器，提升检测准确率
- 可解释异常判定，支持特征重要性分析，适合需要可解释性的场景
我们提出一种基于无监督随机森林的相似性学习方法，以改进无监督异常检测。通过训练随机森林区分真实数据与在真实数据边界内均匀采样的合成数据，获得一个各向异性变换后的距离度量，该度量在数据流形边界处扩大距离。实验表明，利用此变换恢复的距离能显著提升无监督异常检测的准确性，优于多种常用检测器，在大量基准数据集上得到验证。该方法还具有预处理要求低、原生支持缺失数据、易于可视化等优势。通过将异常得分与随机森林的划分路径关联，我们进一步实现了局部可解释的异常预测，提供特征重要性解释。
原文摘要 · Abstract (English)
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a uniform distribution over the real data bounds, a distance measure is obtained that anisometrically transforms the data, expanding distances at the boundary of the data manifold. We show that using distances recovered from this transformation improves the accuracy of unsupervised anomaly detection, compared to other commonly used detectors, demonstrated over a large number of benchmark datasets. As well as improved performance, this method has advantages over other unsupervised anomaly detection methods, including minimal requirements for data preprocessing, native handling of missing data, and potential for visualizations. By relating outlier scores to partitions of the Random Forest, we develop a method for locally explainable anomaly predictions in terms of feature importance.
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