The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Subsampling for efficient and effective unsupervised outlier detection ensembles
Abstract
Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Here, we propose and study subsampling as a technique to induce diversity among individual outlier detectors. We show analytically and experimentally that an outlier detector based on a subsample per se, besides inducing diversity, can, under certain conditions, already improve upon the results of the same outlier detector on the complete dataset. Building an ensemble on top of several subsamples is further improving the results. While in the literature so far the intuition that ensembles improve over single outlier detectors has just been transferred from the classification literature, here we also justify analytically why ensembles are also expected to work in the unsupervised area of outlier detection. As a side effect, running an ensemble of several outlier detectors on subsamples of the dataset is more efficient than ensembles based on other means of introducing diversity and, depending on the sample rate and the size of the ensemble, can be even more efficient than just the single outlier detector on the complete data.
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Cites
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
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-citationHansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Self-citationHawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
A 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
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Cited by
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-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
A decomposition of the outlier detection problem into a set of supervised learning problems
Paulheim, H., & Meusel, R. (2015) · Machine Learning · doi:10.1007/s10994-015-5507-y · Cited by 62
Pang, G., Cao, L., Chen, L., Lian, D., & Liu, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11692 · Cited by 59
Beyond von Neumann Era: Brain-Inspired Hyperdimensional Computing to the Rescue
Amrouch, H., Genssler, P. R., Imani, M., Issa, M., Jiao, X., Mohammad, W., Sepanta, G., & Wang, R. (2023) · Proceedings of the 28th Asia and South Pacific Design Automation Conference · doi:10.1145/3566097.3568354 · Cited by 1
Metadata
- Authors
- Zimek, A., Gaudet, M., Campello, R. J. G. B., & Sander, J.
- Venue
- Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 428-436
- OpenAlex
- W1990106316
- Cited by
- 190
- References
- 6
- FWCI
- 7.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zimek, A., Gaudet, M., Campello, R. J. G. B., & Sander, J. (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2487575.2487676
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