A decomposition of the outlier detection problem into a set of supervised learning problems
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References 44 · Cited by 62
Research works printed but not linked: 5
- Amer, M., & Goldstein, M. (2012). Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer. In Proceedings of the 3rd RapidMiner community meeting and conference (RCOMM 2012) (pp. 1-12).
- Goldstein, M. (2014). Anomaly detection. In M. Hofmann & R. Klinkenberg (Eds.), RapidMiner-Data mining use cases and business analytics applications (pp. 409-436). CRC Press.
- Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009). Loop: Local outlier probabilities. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 1649-1652). ACM.
- Nemenyi, P. (1962). Distribution-free multiple comparisons. Biometrics, 18(2), 263.
- Quinlan, J. R., et al. (1992). Learning with continuous classes. In Proceedings of the 5th Australian joint conference on artificial intelligence (Vol. 92, pp. 343-348).
- Authors
- Paulheim, H., & Meusel, R.
- Affiliations
- University of Mannheim (DE)
- Corresponding author
- Paulheim, Heiko
- ORCID
- Paulheim, Heiko
- Venue
- Machine Learning
- Published
- 2015-06-20
- Volume
- 100
- Issue
- 2-3
- Pages
- 509-531
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W599384242
- Cited by
- 62
- References
- 44
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 5 · 2020: 13 · 2019: 5 · 2018: 5 · 2017: 4 · 2016: 3 · 2015: 1
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- MADOC (University of Mannheim) (submittedVersion)
- Funders
- Technische Universität Darmstadt
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- outlier detection, unsupervised learning, regression analysis, feature weighting, outlier score, high-dimensional data, irrelevant attributes, symbolic machine learning, explanation generation, numerical data
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