# Explainable multi-class anomaly detection on functional data

arXiv: https://arxiv.org/abs/2205.02935 · HTML: https://mlsanity.com/paper/2205.02935/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Beyond Sparsity: Tree Regularization of Deep Models for Interpretability](https://mlsanity.com/paper/1711.06178/) (2017)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [Functional Isolation Forest](https://mlsanity.com/paper/1904.04573/) (2019)
- [MacroBase: Prioritizing Attention in Fast Data](https://mlsanity.com/paper/1603.00567/) (2016)

## Built on by (most cited first, 1 of 1)

- [Distance Rank Score: Unsupervised filter method for feature selection on imbalanced dataset](https://mlsanity.com/paper/2305.19804/) (2023)

## Abstract (arXiv)

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.