Using multiple classifier behavior to develop a dynamic outlier ensemble
Author:
Funder
Key Programme
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s13042-020-01183-7.pdf
Reference36 articles.
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2. Aggarwal CC, Sathe S (2017) Outlier ensembles. Springer, Berlin
3. Ando S, Thanomphongphan T, Seki Y, Suzuki E (2015) Ensemble anomaly detection from multi-resolution trajectory features. Data Min Knowl Disc 29(1):39–83
4. Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Houle ME (2016) On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min Knowl Disc 30(4):891–927
5. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58
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