Nikunj C. Oza's Publications

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

nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique. Santanu Das, Kanishka Bhaduri, Nikunj Oza, and Ashok Srivastava. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2009.

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Abstract

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.

BibTeX Entry

@inproceedings{dabh09,
	author = {Santanu Das, Kanishka Bhaduri, Nikunj Oza, and Ashok Srivastava},
	title = {nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique},
	booktitle={Proceedings of the IEEE International Conference on Data Mining (ICDM)},
	abstract={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.},
	bib2html_pubtype = {Refereed Conference},
	bib2html_rescat = {Anomaly Detection},
	year = {2009}
}

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