Work

Explainable multi-class anomaly detection on functional data

Year: 2022

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2205.02935

Authors: Cura, Mathieu, Firdova, Katarina, Labart, Céline, Martel, Arthur

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4229446375

Cites:

Cited by: 1

Related to: 10