Abstract
In this paper we propose a solution to deal with the problem of novelty detection. Given a set of training examples believed to come from the same class, the aim is to learn a model that will be able to distinguich examples in the future that do not belong to the same class. The proposed approach called Selected Random Subspace Novelty Detection Filter (SRS − NDF) is based on the bootstrap technique, the ensemble idea and model selection principle. The SRS − NDF method is compared to novelty detection methods on publicly available datasets. The results show that for most datasets, this approach significantly improves performance over current techniques used for novelty detection.
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Hamdi, F. (2013). Selected Random Subspace Novelty Detection Filter. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_43
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DOI: https://doi.org/10.1007/978-3-642-42054-2_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42053-5
Online ISBN: 978-3-642-42054-2
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