The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Interpreting and Unifying Outlier Scores
Abstract
Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary “outlier factors” to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.
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Cites
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
Self-citationThe class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
The Comparison and Evaluation of Forecasters
DeGroot, M. H., & Fienberg, S. E. (1983) · The Statistician · doi:10.2307/2987588 · Cited by 604 · cited 2×
“, the forecast probabilities should be concentrated near 1 and near 0 (still being correlated with the actual occurrence of the forecast event) [13, 47].”
BackgroundReliability of Subjective Probability Forecasts of Precipitation and Temperature
Murphy, A. H., & Winkler, R. L. (1977) · Applied Statistics · doi:10.2307/2346866 · Cited by 386
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×
“An optimally calibrated prediction algorithm is expected to be correct in a portion x of all predictions that are subject to a confidence value of x [10].”
Method
Cited by
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915
Self-citationA survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864
Self-citationProgress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection
Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3040950 · Cited by 389
Metadata
- Authors
- Kriegel, H.-P., Kroger, P., Schubert, E., & Zimek, A.
- Venue
- Proceedings of the 2011 SIAM International Conference on Data Mining
- Published
- 2011-04-28
- Pages
- 13-24
- OpenAlex
- W164607750
- Cited by
- 266
- References
- 8
- FWCI
- 9.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kriegel, H.-P., Kroger, P., Schubert, E., & Zimek, A. (2011). Interpreting and Unifying Outlier Scores. Proceedings of the 2011 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611972818.2
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