Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events

  • B. John Oommen
  • Colin Bellinger
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 13)


In this chapter we report some Machine Learning (ML) and Pattern Recognition (PR) techniques applicable for classifying Stochastically Episodic (SE) events1. Researchers in the field of Pattern Recognition (PR) have traditionally presumed the availability of a representative set of data drawn from the classes of interest, say ω 1 and ω 2 in a 2-class problem. These samples are typically utilized in the development of the system’s discriminant function. It is, however, widely recognized that there exists a particularly challenging class of PR problems for which a representative set is not available for the second class, which has motivated a great deal of research into the so-called domain of One Class (OC) classification. In this chapter, we primarily report the novel results found in [2, 4, 6], where we extend the frontiers of novelty detection by the introduction of a new field of problems open for analysis. In particular, we note that this new realm deviates from the standard set of OC problems based on the presence of three characteristics, which ultimately amplify the classification challenge. They involve the temporal nature of the appearance of the data, the fact that the data from the classes are “interwoven”, and that a labelling procedure is not merely impractical - it is almost, by definition, impossible. As a first attempt to tackle these problems, we present two specialized classification strategies denoted by Scenarios S1 and S2 respectively. In Scenarios S1, the data is such that standard binary and one-class classifiers can be applied. Alternatively, in Scenarios S2, the labelling challenge prevents the application of binary classifiers, and instead dictates the novel application of one-class classifiers. The validity of these scenarios has been demonstrated for the exemplary domain involving the Comprehensive Nuclear Test-Ban-Treaty (CTBT), for which our research endeavour has also developed a simulation model. As far as we know, our research in this field is of a pioneering sort, and the results presented here are novel.


Pattern Recognition Rare Events Stochastic Events Erroneous Data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aha, D.W.: Generalizing from case studies: A case study. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 1–10 (1992)Google Scholar
  2. 2.
    Bellinger, C.: Modelling and classifying stochastically episodic events. Master’s thesis, Carleton University, Ottawa, Ontario (2010)Google Scholar
  3. 3.
    Bellinger, C., Japkowicz, N.: Motivating the inclusion of meteorological indicators in the ctbt feature-space. In: Proceedings of IEEE Symposium on Computational Intelligence for Security and Defense Applications, Paris, France (April 2011)Google Scholar
  4. 4.
    Bellinger, C., Oommen, B.J.: A new frontier in novelty detection: Pattern recognition of stochastically episodic events. In: Asian Conference on Intelligent Information and Database Systems (2011)Google Scholar
  5. 5.
    Bellinger, C., Oommen, B.J.: On the pattern recognition and classification of stochastically episodic events. In: Transactions on Computational Collective Intelligence (2011) (accepted for Publication)Google Scholar
  6. 6.
    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 2010)Google Scholar
  7. 7.
    Chapelle, O., Schölkopf, B., Zien, A.: Semi-supervised Learning. MIT Press (2006)Google Scholar
  8. 8.
    Datta, P.: Characteristic concept representations. PhD thesis, University of California at Irvine, Irvine, CA, USA (1997)Google Scholar
  9. 9.
    Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31–71 (1997)MATHCrossRefGoogle Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)MATHGoogle Scholar
  11. 11.
    Ghosh, A.K., Schwartzbard, A., Schatz, M.: Learning program behavior profiles for intrusion detection. In: Proceedings of the Workshop on Intrusion Detection and Network Monitoring, vol. 1, pp. 51–62 (April 1999)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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), doi:10.1007/978-3-540-87479-9CrossRefGoogle Scholar
  14. 14.
    Japkowicz, N.: Concept-Learning in the Absence of Counter-Examples: An Autoassociation-Based Approach to Classication. PhD thesis, Rutgers University (1999)Google Scholar
  15. 15.
    Japkowicz, N., Shah, N.: Evaluating Learning Algorithms: A Classication Perspective. Cambridge University Press (2011)Google Scholar
  16. 16.
    Kubat, M., Holte, R.C., Matwin, S.: Machine learning for the detection of oil spills in satellite radarimages. Machine learning 30(2), 195–215 (1998)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    Mitchell, T.M.: Machine learning. McGraw-Hill (1997)Google Scholar
  19. 19.
    Nairac, A., Townsend, N., Carr, R., King, S., Cowley, P., Tarassenko, L.: A system for the analysis of jet engine vibration data. Integrated Computer-Aided Engineering 6(1), 53–66 (1999)Google Scholar
  20. 20.
    Platzer, E.S., Nägele, J., Wehking, K.H., Denzler, J.: HMM-based defect localization in wire ropes–A new approach to unusual subsequence recognition. Pattern Recognition, 442–451 (2009)Google Scholar
  21. 21.
    Saey, P.R.J.: The influence of radiopharmaceutical isotope production on the global radioxenon background. Journal of Environmental Radioactivity 100(5), 396–406 (2009)CrossRefGoogle Scholar
  22. 22.
    Saey, P.R.J., Bowyer, T.W., Ringbom, A.: Isotopic noble gas signatures released from medical isotope production facilities – Simulation and measurements. Applied Radiation and Isotpes (2010)Google Scholar
  23. 23.
    Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, vol. 12, pp. 582–588 (2000)Google Scholar
  24. 24.
    Stocki, T.J., Japkowicz, N., Li, G., Ungar, R.K., Hoffman, I., Yi, J.: In: Summary of the Data mining Contest for the IEEE International Conference on Data Mining, Pisa, Italy (2008)Google Scholar
  25. 25.
    Tarassenko, L., Hayton, P., Cerneaz, N., Brady, M.: Novelty detection for the identification of masses in mammograms. In: IEE Conference Publications, (CP 409), pp. 442–447 (1995)Google Scholar
  26. 26.
    Tax, D.M.J.: One-class classification; Concept-learning in the absence of counter-examples. PhD thesis, Technische Universiteit Delft, Netherlands (2001)Google Scholar
  27. 27.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers (2005)Google Scholar
  28. 28.
    Xu, R., Wunsch, D.C.: Clustering. Wiley-IEEE Press (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer ScienceCarleton UniversityOttawaCanada
  2. 2.The School of Information Technology and EngineeringUniversity of OttawaOttawaCanada

Personalised recommendations