Fast anomaly detection for streaming data

Title: Fast anomaly detection for streaming data
Creator: Tan, Swee; Ting, Kaiming; Liu, Fei
Date: 2011
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/74348
Identifier: vital:7257
Identifier: https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-254
Identifier: ISBN:9781577355120
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.
Publisher: AAAI Press
Relation: International Joint Conference on Artificial Intelligence (IJCAI) p. 1511-1516
Rights: This metadata is freely available under a CCO license
Subject: 0801 Artificial Intelligence and Image Processing
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