- Title
- LeSiNN : Detecting anomalies by identifying least similar nearest neighbours
- Creator
- Pang, Guansong; Ting, Kaiming; Albrecht, David
- Date
- 2015
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/101211
- Identifier
- vital:10664
- Abstract
- We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time complexity with respect to data size and the number of dimensions, and it is one of the few anomaly detectors which can apply directly to both numeric and categorical data sets. Our extensive empirical evaluation shows that LeSiNN is either competitive to or better than six state-of-the-art anomaly detectors in terms of detection accuracy and runtime. © 2015 IEEE.; Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015; Atlantic City, New Jersey; 14th-17th November 2015; published in Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
- Rights
- Copyright © 2015 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Anomaly Detection; Ensemble; kNN; Least Similar Nearest Neighbours
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