论文标题
可解释功能数据的多类异常检测
Explainable multi-class anomaly detection on functional data
论文作者
论文摘要
在本文中，我们描述了一种异常检测方法及其在多元功能数据中的解释性。异常检测过程包括将系列转换为特征向量并使用隔离森林算法。可解释的过程基于外形系数的计算和使用监督决策树的使用。我们将其应用于模拟数据，以衡量我们的方法的性能以及来自行业的真实数据。
In this paper we describe an approach for anomaly detection and its explainability in multivariate functional data. The anomaly detection procedure consists of transforming the series into a vector of features and using an Isolation forest algorithm. The explainable procedure is based on the computation of the SHAP coefficients and on the use of a supervised decision tree. We apply it on simulated data to measure the performance of our method and on real data coming from industry.
