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ν-Anomica: A Fast Support Vector Based Novelty Detection Technique

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4 Author(s)
Das, S. ; UARC, UC, Santa Cruz, CA, USA ; Bhaduri, K. ; Oza, N.C. ; Srivastava, A.N.

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.

Published in:

Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on

Date of Conference:

6-9 Dec. 2009