A Framework for Outlier Description Using Constraint Programming
Open accessAbstract
Outlier detection has been studied extensively and employed in diverse applications in the past decades. In this paper we formulate a related yet understudied problem which we call outlier description. This problem often arises in practice when we have a small number of data instances that had been identified to be outliers and we wish to explain why they are outliers. We propose a framework based on constraint programming to find an optimal subset of features that most differentiates the outliers and normal instances. We further demonstrate the framework offers great flexibility in incorporating diverse scenarios arising in practice such as multiple explanations and human in the loop extensions. We empirically evaluate our proposed framework on real datasets, including medical imaging and text corpus, and demonstrate how the results are useful and interpretable in these domains.
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The method in [110] is only applicable to Isolation Forest.
“Only applicable to Isolation Forest”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [110]
The Introduction (p. 1) says the method addresses cases where outliers are already known and seeks to describe what makes them anomalous. The framework section (p. 2) specifies a feature-subspace and local-density approach, not a method restricted to Isolation Forest. (LLM, conf=0.89)
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Research works printed but not linked: 1
- Knorr, E. M.; Ng, R. T.; and Tucakov, V. 2000. Distancebased outliers: algorithms and applications. VLDB 8(34):237-253.
Cited by
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A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139 · see citing works
“Concretely, Kuo & Davidson [110] formally define the outlier description (namely anomaly explanation) problem and propose a Constraint Programming (CP) based framework to encode the problem.”
Background
- Authors
- Kuo, C.-T., & Davidson, I.
- Affiliations
- University of California, Davis (US)
- ORCID
- Davidson, Ian
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2016-02-21
- Volume
- 30
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2564534161
- Cited by
- 18
- References
- 23
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2019: 4 · 2018: 3 · 2017: 3
- FWCI
- 1.80
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (1422218)
- Office of Naval Research (N00014-11-1-0108, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- outlier detection, constraint programming, feature selection, explainable AI, multiple explanations, human-in-the-loop, diagnostic imaging, text corpora
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