The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Open accessAbstract
Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet effective algorithm calledECOD(Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the “rare events” that appear in the tails of a distribution. In a nutshell,ECODfirst estimates the underlying distribution of the input data in a nonparametric fashion by computing the empirical cumulative distribution per dimension of the data.ECODthen uses these empirical distributions to estimate tail probabilities per dimension for each data point. Finally,ECODcomputes an outlier score of each data point by aggregating estimated tail probabilities across dimensions. Our contributions are as follows: (1) we propose a novel outlier detection method calledECOD, which is both parameter-free and easy to interpret; (2) we perform extensive experiments on 30 benchmark datasets, where we find thatECODoutperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and scalability; and (3) we release an easy-to-use and scalable (with distributed support) Python implementation for accessibility and reproducibility.
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Cites
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“This has motivated commonly used heuristic OD approaches such as the “three-sigma” rule that declares points more than three standard deviations from the mean to be outliers ([27], [28]; there’s also a robust variant called the “1.5 IQR” rule [29]).”
MethodInfluentialDeep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342 · cited 1×
“We report both the raw comparison results and the critical difference (CD) plots to show statistical difference [55], [56], where it visualize the statistical comparison by Wilcoxon signed-rank test with Holm’s correction.”
MethodDeep 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
Efficient algorithms for mining outliers from large data sets
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Cited by
Multimodal Industrial Anomaly Detection via Hybrid Fusion
Wang, Y., Peng, J., Zhang, J., Yi, R., Wang, Y., & Wang, C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00776 · Cited by 246
Unsupervised detection of coordinated fake-follower campaigns on social media
Zouzou, Y., & Varol, O. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00499-6 · Cited by 25
Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines
Tu, D., He, Y., Cui, W., Ge, S., Zhang, H., Han, S., Zhang, D., & Chaudhuri, S. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599776 · Cited by 14
Metadata
- Authors
- Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. H.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2023-12-01
- Volume
- 35
- Issue
- 12
- Pages
- 12181-12193
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W4226060502
- Cited by
- 494
- References
- 52
- FWCI
- 29.49
- Type
- journal article
- Language
- en
- Open access
- green
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- Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. H. (2023). ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2022.3159580
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