A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection. A classical approach to the problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the constructed description of the normal class. In this paper we present a new approach to the one-class classification. We formulate a new problem statement and a corresponding algorithm that allow taking into account a privileged information during the training phase. We evaluate performance of the proposed approach using a synthetic dataset, as well as the publicly available Microsoft Malware Classification Challenge dataset.
@article{arxiv.1609.08039,
title = {One-Class SVM with Privileged Information and its Application to Malware Detection},
author = {Evgeny Burnaev and Dmitry Smolyakov},
journal= {arXiv preprint arXiv:1609.08039},
year = {2016}
}