- Title
- Efficient anomaly detection by isolation using Nearest Neighbour Ensemble
- Creator
- Bandaragoda, Tharindu; Ting, Kaiming; Albrecht, David; Liu, Fei; Wells, Jonathan
- Date
- 2014
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160079
- Identifier
- vital:12085
- Identifier
-
https://doi.org/10.1109/ICDMW.2014.70
- Identifier
- ISBN:978-1-4799-4274-9
- Abstract
- This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbour-based anomaly detection method by isolation. Inne runs significantly faster than existing nearest neighbour-based methods such as Local Outlier Factor, especially in data sets having thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and constant space complexity. Compared with the existing tree-based isolation method iForest, the proposed isolation method overcomes three weaknesses of iForest that we have identified, i.e., Its inability to detect local anomalies, anomalies with a low number of relevant attributes, and anomalies that are surrounded by normal instances.
- Publisher
- Conference Publishing Services
- Relation
- 14th IEEE International Conference on Data Mining Workshop (ICDMW 2014); Shenzhen, China; 14th December 2014 p. 698-705
- Rights
- Copyright © 2014 by The Institute of Electrical and Electronics Engineers, Inc.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Detectors; Time Complexity; Training; Educational Institutions; Accuracy; Estimation; Bismuth; Ensemble-Based; Anomaly Detection; Nearest-Neighbour; Computing and Processing
- Reviewed
- Hits: 1602
- Visitors: 1385
- Downloads: 1
| Thumbnail | File | Description | Size | Format |
|---|