Nikunj Oza

Member since: Sep 30, 2010, NASA

nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique

Shared by Nikunj Oza, updated on Mar 12, 2012


Author(s) :
Santanu Das, Kanishka Bhaduri, Nikunj Oza, Ashok Srivastava

In this paper we propose $\nu$-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 $\nu$-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.

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Publication Name
IEEE International Conference on Data Mining (ICDM)
Publication Location
Miami Beach, FL, USA
Year Published


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