How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?

TL;DR

This paper proposes label-free criteria based on EM and MV curves, with a feature sub-sampling method, to evaluate unsupervised anomaly detection algorithms, especially in high-dimensional data where traditional methods struggle.

Contribution

It introduces a novel methodology using feature sub-sampling and aggregation to effectively estimate EM and MV criteria in high-dimensional datasets for unsupervised anomaly detection evaluation.

Findings

Criteria accurately discriminate algorithms without labels

Feature sub-sampling extends applicability to high-dimensional data

Method outperforms traditional EM and MV curve estimations

Abstract

When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms. However , in many situations, few or no data are labeled. This calls for alternative criteria one can compute on non-labeled data. In this paper, two criteria that do not require labels are empirically shown to discriminate accurately (w.r.t. ROC or PR based criteria) between algorithms. These criteria are based on existing Excess-Mass (EM) and Mass-Volume (MV) curves, which generally cannot be well estimated in large dimension. A methodology based on feature sub-sampling and aggregating is also described and tested, extending the use of these criteria to high-dimensional datasets and solving major drawbacks inherent to standard EM and MV curves.