Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arya, S.P.: Air Pollution Meteorology and Dispersion. Oxford University Press, New York (1999)
Bellinger, C., Oommen, B.J.: On simulating episodic events against a background of noise-like non-episodic events. In: Proceedings of 42nd Summer Computer Simulation Conference, SCSC 2010, Ottawa, Canada, July 11-14 (2010)
Bellinger, C., Oommen, B.J.: On the pattern recognition and classification of stochastically episodic events. Transactions on Computational Collective Intelligence (2011) (accepted for publication)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006)
Fontaine, J., Pointurier, F., Blanchard, X., Taffary, T.: Atmospheric xenon radioactive isotope monitoring. Journal of Environmental Radioactivity 72, 129–135 (2004)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Hempstalk, K., Frank, E., Witten, I.H.: One-Class Classification by Combining Density and Class Probability Estimation. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 505–519. Springer, Heidelberg (2008)
Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press (2011)
Japkowicz, N.: Supervised versus unsupervised binary-learning by feedforward neural networks. Machine Learning 42(1/2), 97–122 (2001)
Kubat, M., Holte, R.C., Matwin, S.: Machine learning for the detection of oil spills in satellite radar images. In: Machine Learning, pp. 195–215 (1998)
Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided selection. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179–186. Morgan Kaufmann (1997)
Manevitz, L.M., Yousef, M.: One-class svms for document classification. The Journal of Machine Learning Research 2, 139–154 (2002)
Matwin, S., Kouznetsov, A., Inkpen, D., Frunza, O., O’Blenis, P.: A new algorithm for reducing the workload of experts in performing systematic reviews. Journal of the American Medical Informatics Association 17, 446–453 (2010)
Simmonds, J.R., Lawson, G., Mayall, A.: A Methodology for Assessing the Radiological Consequences of RoutineReleases of Radionuclides to the Environment. EUR, 1018-5593; 15760. European Commission, Directorate-General for Environment, Nuclear Safety and Civil Protection (1995)
Stocki, T.J., Japkowicz, N., Ungar, I.K., Hoffman, J., Yi, J.: Summary of the data mining contest for the IEEE international conference on data mining. In: Proceedings of the ICDM 2008 Data Mining Contest (2008), http://www.cs.uu.nl/groups/ADA/icdm08cup/booklet.pdf
Stocki, T.J., Li, G., Japkowicz, N., Ungar, R.K.: Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty. Journal of Environmental Radioactivity 101(1), 68–74 (2010)
Sullivan, J.D.: The comprehensive test ban treaty. Physics Today 51(3), 24–29 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sharma, S., Bellinger, C., Japkowicz, N. (2012). Clustering Based One-Class Classification for Compliance Verification of the Comprehensive Nuclear-Test-Ban Treaty. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-30353-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30352-4
Online ISBN: 978-3-642-30353-1
eBook Packages: Computer ScienceComputer Science (R0)