mlsanityData to 14 Oct 2026

Toward Unsupervised Outlier Model Selection

arXiv 2211.01834 ↗First posted 3 Nov 2022cs.LGcited by 19

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Abstract (arXiv)

Today there exists no shortage of outlier detection algorithms in the literature, yet the complementary and critical problem of unsupervised outlier model selection (UOMS) is vastly understudied. In this work we propose ELECT, a new approach to select an effective candidate model, i.e. an outlier detection algorithm and its hyperparameter(s), to employ on a new dataset without any labels. At its core, ELECT is based on meta-learning; transferring prior knowledge (e.g. model performance) on historical datasets that are similar to the new one to facilitate UOMS. Uniquely, it employs a dataset similarity measure that is performance-based, which is more direct and goal-driven than other measures used in the past. ELECT adaptively searches for similar historical datasets, as such, it can serve an output on-demand, being able to accommodate varying time budgets. Extensive experiments show that ELECT significantly outperforms a wide range of basic UOMS baselines, including no model selection (always using the same popular model such as iForest) as well as more recent selection strategies based on meta-features.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 15 of its 49 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

15 of 49 references.

References · 15

  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
  2. 2019AutoML: A Survey of the State-of-the-Artcited by 390
  3. 2018Meta-Learning: A Surveycited by 256
  4. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  5. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  6. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 182
  7. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  8. 2018Model Evaluation, Model Selection, and Algorithm Selection in Machine Learningcited by 157
  9. 2022Constrained Efficient Global Optimization of Expensive Black-box Functionscited by 49
  10. 2019Unsupervised Model Selection for Variational Disentangled Representation Learningcited by 46
  11. 2020Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Datacited by 25
  12. 2016How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?cited by 24
3 more
  1. 2020PyODDS: An End-to-end Outlier Detection System with Automated Machine Learningcited by 21
  2. 2021A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?cited by 8
  3. 2017Hybrid Isolation Forest - Application to Intrusion Detectioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 5; 2025: 4; 2026: 3.

19 papers.

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  1. 2024MetaOOD: Automatic Selection of OOD Detection Modelscited by 16
  2. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  3. 2025ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Modelscited by 9
  4. 2026From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detectioncited by 9
  5. 2023Model Selection of Anomaly Detectors in the Absence of Labeled Validation Datacited by 8
  6. 2024On the Detection of Reviewer-Author Collusion Rings From Paper Biddingcited by 7
  7. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
  8. 2023DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selectioncited by 6
  9. 2023Fast Unsupervised Deep Outlier Model Selection with Hypernetworkscited by 6
  10. 2023Towards Robust Multi-Modal Reasoning via Model Selectioncited by 6
  11. 2024Ranked from Within: Ranking Large Multimodal Models Without Labelscited by 5
  12. 2023Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunitiescited by 2
7 more
  1. 2024Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)cited by 1
  2. 2023Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?cited by 0
  3. 2024End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detectioncited by 0
  4. 2025UniOD: A Universal Model for Outlier Detection across Diverse Domainscited by 0
  5. 2025Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domainscited by 0
  6. 2026Automatic Unsupervised Ensemble Outlier Model Selection--Extended Versioncited by 0
  7. 2026OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detectioncited by 0