Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty

  • Shiven Sharma
  • Colin Bellinger
  • Nathalie Japkowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)


Monitoring the levels of radioxenon isotopes in the atmosphere has been proposed as a means of verifying the Comprehensive Nuclear-Test-Ban Treaty (CTBT). This translates into a classification problem, whereby the measured concentrations either belong to an explosion class or a background class. Instances drawn from the explosions class are extremely rare, if not non-existent. Therefore, the resulting dataset is extremely imbalanced, and inherently suited for one-class classification. Further exacerbating the problem is the fact that the background distribution can be extremely complex, and thus, modelling it using one-class learning is difficult. In order to improve upon the previous classification results, we investigate the augmentation of one-class learning methods with clustering. The purpose of clustering is to convert a complex distribution into simpler distributions, the clusters, over which more effective models can be built. The resulting model, built from one-class learners trained over the clusters, performs more effectively than a model that is built over the original distribution. This thesis is empirically tested on three different data domains; in particular, a number of artificial datasets, datasets from the UCI repository, and data modelled after the extremely challenging CTBT. The results offer credence to the fact that there is an improvement in performance when clustering is used with one-class classification on complex distributions.


Target Class Original Distribution Complex Distribution Multimodal Distribution Imbalance Ratio 
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 2012

Authors and Affiliations

  • Shiven Sharma
    • 1
  • Colin Bellinger
    • 1
  • Nathalie Japkowicz
    • 1
  1. 1.SITEUniversity of OttawaOttawaCanada

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