The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Feature bagging for outlier detection
Abstract
Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel feature bagging approach for detecting outliers in very large, high dimensional and noisy databases is proposed. It combines results from multiple outlier detection algorithms that are applied using different set of features. Every outlier detection algorithm uses a small subset of features that are randomly selected from the original feature set. As a result, each outlier detector identifies different outliers, and thus assigns to all data records outlier scores that correspond to their probability of being outliers. The outlier scores computed by the individual outlier detection algorithms are then combined in order to find the better quality outliers. Experiments performed on several synthetic and real life data sets show that the proposed methods for combining outputs from multiple outlier detection algorithms provide non-trivial improvements over the base algorithm.
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Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
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
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
When Is ''Nearest Neighbor'' Meaningful?
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Robust classification for imprecise environments
Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250
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
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Deng, A., & Hooi, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16523 · Cited by 1,413
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
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
Metadata
- Authors
- Lazarevic, A., & Kumar, V.
- Venue
- Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
- Publisher
- ACM
- Published
- 2005-08-21
- Pages
- 157-166
- OpenAlex
- W2056081083
- Cited by
- 656
- References
- 5
- FWCI
- 7.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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