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2009 Ninth IEEE International Conference on Data Mining (2009)
Miami, Florida
Dec. 6, 2009 to Dec. 9, 2009
ISSN: 1550-4786
ISBN: 978-0-7695-3895-2
pp: 101-109
In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In ν-Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one- class Support Vector Machines while reducing both the training time and the test time by 5 − 20 times.
Anomaly Detection, Support Vector Machines, Kernel, Optimization

N. C. Oza, K. Bhaduri, S. Das and A. N. Srivastava, "?-Anomica: A Fast Support Vector Based Novelty Detection Technique," 2009 Ninth IEEE International Conference on Data Mining(ICDM), Miami, Florida, 2009, pp. 101-109.
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