mlsanityData to 7 Oct 2026

How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?

arXiv 1607.01152 ↗First posted 5 Jul 2016stat.ML cs.LGcited by 22

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Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2016.

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Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 3; 2025: 3; 2026: 3.

22 papers.

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  1. 2022Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solutioncited by 24
  2. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
  3. 2022Toward Unsupervised Outlier Model Selectioncited by 18
  4. 2023Unsupervised Anomaly Detection with Rejectioncited by 9
  5. 2021A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?cited by 8
  6. 2022Graph Anomaly Detection with Unsupervised GNNscited by 8
  7. 2023Fast Unsupervised Deep Outlier Model Selection with Hypernetworkscited by 6
  8. 2022Meta-Learning for Unsupervised Outlier Detection with Optimal Transportcited by 5
  9. 2022Hyperparameter Optimization for Unsupervised Outlier Detectioncited by 4
  10. 2025Leveraging the Christoffel Function for Outlier Detection in Data Streamscited by 4
  11. 2021Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Methodcited by 3
  12. 2023Is AUC the best measure for practical comparison of anomaly detectors?cited by 2
10 more
  1. 2023Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunitiescited by 2
  2. 2024Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyondcited by 1
  3. 2024Towards Unsupervised Validation of Anomaly-Detection Modelscited by 1
  4. 2023Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stoppingcited by 0
  5. 2024Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measurescited by 0
  6. 2025Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detectioncited by 0
  7. 2025Automated Machine Learning for Unsupervised Tabular Taskscited by 0
  8. 2026Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementaritycited by 0
  9. 2026Automatic Unsupervised Ensemble Outlier Model Selection--Extended Versioncited by 0
  10. 2026Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehiclescited by 0