Explainable multi-class anomaly detection on functional data

Autor:	Cura, Mathieu, Firdova, Katarina, Labart, Céline, Martel, Arthur
Rok vydání:	2022
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Mathematics - Statistics Theory 62P30 62H30
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.02935 Zobrazit plný text záznamu View this record from Arxiv