- Title
- Isolation-based anomaly detection using nearest-neighbor ensembles
- Creator
- Bandaragoda, Tharindu; Ting, Kaiming; Albrecht, David; Liu, Fei; Zhu, Ye; Wells, Jonathan
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166726
- Identifier
- vital:13502
- Identifier
-
https://doi.org/10.1111/coin.12156
- Identifier
- ISBN:0824-7935
- Abstract
- The first successful isolation-based anomaly detector, ie, iForest, uses trees as a means to perform isolation. Although it has been shown to have advantages over existing anomaly detectors, we have identified 4 weaknesses, ie, its inability to detect local anomalies, anomalies with a high percentage of irrelevant attributes, anomalies that are masked by axis-parallel clusters, and anomalies in multimodal data sets. To overcome these weaknesses, this paper shows that an alternative isolation mechanism is required and thus presents iNNE or isolation using Nearest Neighbor Ensemble. Although relying on nearest neighbors, iNNE runs significantly faster than the existing nearest neighbor–based methods such as the 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. © 2018 Wiley Periodicals, Inc.
- Publisher
- Blackwell Publishing Inc.
- Relation
- Computational Intelligence Vol. 34, no. 4 (2018), p. 968-998
- Rights
- Copyright © 2018 Wiley Periodicals, Inc.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; 0806 Information Systems; Anomaly detection; Ensemble learning; Isolation-based; Nearest neighbor; Outlier detection
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