Fast anomaly detection for streaming data. Tan, S. C., Ting, K. M., & Liu, T. F. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two, of IJCAI'11, pages 1511–1516, Barcelona, Catalonia, Spain, July, 2011. AAAI Press. abstract bibtex This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-the-art method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.
@inproceedings{tan_fast_2011,
address = {Barcelona, Catalonia, Spain},
series = {{IJCAI}'11},
title = {Fast anomaly detection for streaming data},
isbn = {978-1-57735-514-4},
abstract = {This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-the-art method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.},
urldate = {2021-10-15},
booktitle = {Proceedings of the {Twenty}-{Second} international joint conference on {Artificial} {Intelligence} - {Volume} {Volume} {Two}},
publisher = {AAAI Press},
author = {Tan, Swee Chuan and Ting, Kai Ming and Liu, Tony Fei},
month = jul,
year = {2011},
keywords = {anomaly detection, stream, tree},
pages = {1511--1516},
}
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