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Selected Random Subspace Novelty Detection Filter

  • Fatma Hamdi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

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.

Keywords

Ensemble Member Ensemble Method Multi Layer Perceptron Novelty Detection True Negative Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fatma Hamdi
    • 1
  1. 1.CGI BCFrance

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