Contextual Outlier Interpretation
Open accessAbstract
While outlier detection has been intensively studied in many applications, interpretation is becoming increasingly important to help people trust and evaluate the developed detection models through providing intrinsic reasons why the given outliers are identified. It is a nontrivial task for interpreting the abnormality of outliers due to the distinct characteristics of different detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, contexts where outliers locate, as well as the relation between outliers and the contexts, are usually overlooked in existing interpretation frameworks. To tackle the issues, in this paper, we propose a Contextual Outlier INterpretation (COIN) framework to explain the abnormality of outliers spotted by detectors. The interpretability of an outlier is achieved through three aspects, i.e., outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods. Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework.
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Model-agnostic techniques such as Reference [123] usually have an Explanation-Definition that may differ from the Detection-Definition.
“On the contrary, model-agnostic techniques such as Reference [123] usually have an Explanation-Definition that may differ from the Detection-Definition.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [123]
The Introduction (page unavailable) calls COIN model-agnostic, and §3.1 (page unavailable) describes using local classification models to explain the behavior of a separate outlier detector. That supports COIN as an example whose explanation method may differ from its detection method, but the paper does not establish that model-agnostic techniques *usually* have this property. (LLM, conf=0.82)
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- Authors
- Liu, N., Shin, D., & Hu, X.
- Affiliations
- Texas A&M University (US)
- Texas A&M Engineering Experiment Station (US)
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 2461-2467
- OpenAlex
- W2962819609
- Cited by
- 74
- References
- 32
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 17 · 2023: 12 · 2022: 9 · 2021: 14 · 2020: 6 · 2019: 6 · 2018: 4
- FWCI
- 2.98
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1657196, 1718840)
- Defense Advanced Research Projects Agency (#N66001-17-2-4031)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- outlier detection, explainable AI, contextual explanations, feature attribution, neighborhood context, class imbalance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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