One-Class Semi-supervised Learning

  • Evgeny BaumanEmail author
  • Konstantin Bauman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11100)


One-class classification problem aims to identify elements of a specific class among all other elements. This problem has been extensively studied in the last decade and the developed methods were applied to a large number of different problems, such as outlier detection, natural language processing, fraud detection, and many others. In this work, we developed a new semi-supervised one-class classification algorithm which assumes that the class is linearly separable from other elements. We proved theoretically that the class is linearly separable if and only if it is maximal by probability within the sets of elements with the same mean. Furthermore, we constructed an algorithm for identifying such linearly separable class based on linear programming. We considered three application cases including an assumption of linear separability of the class, Gaussian distribution, and the case of linear separability in the transformed space of kernel functions. Finally, we examined the work of the proposed algorithm on the USPS dataset and analyzed the relationship of its performance and the size of the initially labeled sample.


  1. 1.
    Aizerman, M.A., Braverman, E.A., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. In: Automation and Remote Control, Number 25 in Automation and Remote Control, pp. 821–837 (1964)Google Scholar
  2. 2.
    Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013, pp. 8–15 (2013)Google Scholar
  3. 3.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York (2004)CrossRefGoogle Scholar
  4. 4.
    Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2010)Google Scholar
  5. 5.
    Dreiseitl, S., Osl, M., Scheibböck, C., Binder, M.: Outlier detection with one-class SVMS: an application to melanoma prognosis. In: AMIA Annual Symposium Proceedings/AMIA Symposium, vol. 2010, pp. 172–176. AMIA Symposium (2010)Google Scholar
  6. 6.
    Joffe, E., Pettigrew, E.J., Herskovic, J.R., Bearden, C.F., Bernstam, E.V.: Expert guided natural language processing using one-class classification. J. Am. Med. Inform. Assoc. 22(5), 962–966 (2015)CrossRefGoogle Scholar
  7. 7.
    Kennedy, K., Namee, B.M., Delany, S.J.: Using semi-supervised classifiers for credit scoring. J. Oper. Res. Soc. 64(4), 513–529 (2013)CrossRefGoogle Scholar
  8. 8.
    Khan, S.S., Madden, M.G.: A survey of recent trends in one class classification. In: Coyle, L., Freyne, J. (eds.) AICS 2009. LNCS, vol. 6206, pp. 188–197. Springer, Heidelberg (2010). Scholar
  9. 9.
    Lee, G., Scott, C.D.: The one class support vector machine solution path. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP 2007, vol. 2, pp. II-521–II-524 (2007)Google Scholar
  10. 10.
    Li, W., Guo, Q., Elkan, C.: A positive and unlabeled learning algorithm for one-class classification of remote-sensing data. IEEE Trans. Geosci. Remote Sens. 49(2), 717–725 (2011)CrossRefGoogle Scholar
  11. 11.
    Manevitz, L.M., Yousef, M.: One-class SVMS for document classification. J. Mach. Learn. Res. 2, 139–154 (2002)zbMATHGoogle Scholar
  12. 12.
    Muñoz, A., Moguerza, J.M.: One-class support vector machines and density estimation: the precise relation. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 216–223. Springer, Heidelberg (2004). Scholar
  13. 13.
    Munoz-Mari, J., Bovolo, F., Gomez-Chova, L., Bruzzone, L., Camp-Valls, G.: Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Trans. Geosci. Remote Sens. 48(8), 3188–3197 (2010)CrossRefGoogle Scholar
  14. 14.
    Prakash, V.J., Nithya, L.M.: A survey on semi-supervised learning techniques. Int. J. Comput. Trends Technol. (IJCTT) 8(1), 25–29 (2014)CrossRefGoogle Scholar
  15. 15.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefGoogle Scholar
  16. 16.
    Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: Solla, S.A., Leen, T.K., Müller, K. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 582–588. MIT Press (2000)Google Scholar
  17. 17.
    Sundarkumar, G.G., Ravi, V., Siddeshwar, V.: One-class support vector machine based undersampling: application to churn prediction and insurance fraud detection. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–7 (2015)Google Scholar
  18. 18.
    Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)CrossRefGoogle Scholar
  19. 19.
    Vapnik, V.: Transductive inference and semi-supervised learning. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning, Chap. 24, pp. 453–472. MIT Press (2006)Google Scholar
  20. 20.
    Zhang, R., Zhang, S., Muthuraman, S., Jiang, J.: One class support vector machine for anomaly detection in the communication network performance data. In: Conference on Applied Electromagnetics, Wireless and Optical Communications, pp. 31–37 (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Markov Processes Inc.SummitUSA
  2. 2.Fox School of BusinessTemple UniversityPhiladelphiaUSA

Personalised recommendations