K – Means Based One-Class SVM Classifier

  • Loai AbedallaEmail author
  • Murad Badarna
  • Waleed Khalifa
  • Malik Yousef
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1062)


The application of one-class machine learning is gaining attention in the computational biology community. Many biological cases can be considered as multi one-class classification problem. Examples include the classification of multiple cancer types, protein fold recognition and, molecular classification of multiple tumor types. In all of those cases the real world appropriately characterized negative cases or outliers are impractical to be achieved and the positive cases might be consists from different clusters which in turn might reveal to accuracy degradation. In this paper, we present multi-one-class classifier to deal with this problem. The key point of our classification method is to run a clustering algorithm such as the well-known k-means over the positive cases and then building up a classifier for every cluster separately. For a given new example, we apply all the generated classifiers. If it rejected by all of those classifiers, the given example will be considered as a negative case, otherwise it is a positive case.


One class SVM Clustering based classification K-means Ensemble clustering 


  1. 1.
    Kowalczyk, A., Raskutti, B.: One class SVM for yeast regulation prediction. SIGKDD Explor. 4(2), 99–100 (2002)CrossRefGoogle Scholar
  2. 2.
    Spinosa, E.J., Carvalho, A.C.: Support vector machines for novel class detection in Bioinformatics. Genet. Mol. Res. (GMR) 4(3), 608–615 (2005)Google Scholar
  3. 3.
    Crammer, K., Chechik, G.: A needle in a haystack: local one-class optimization. In: Proceedings of the Twenty-First International Conference on Machine Learning (ICML) (2004)Google Scholar
  4. 4.
    Gupta, G., Ghosh, J.: Robust one-class clustering using hybrid global and local search. In: Proceedings of the 22nd International Conference on Machine Learning. ACM Press, Bonn (2005)Google Scholar
  5. 5.
    Yousef, M., Najami, N., Khalifa, W.: A comparison study between one-class and two-class machine learning for MicroRNA target detection. J. Biomed. Sci. Eng. 3, 247 (2010)CrossRefGoogle Scholar
  6. 6.
    Manevitz, L.M., Yousef, M.: One-class SVMs for document classification. J. Mach. Learn. Res. 2, 139–154 (2001)zbMATHGoogle Scholar
  7. 7.
    Thirion, B., Faugeras, O.: Feature characterization in fMRI data: the information bottleneck approach. Med. Image Anal. 8(4), 403 (2004)CrossRefGoogle Scholar
  8. 8.
    Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proceedings of the Twenty-First International Conference on Machine Learning. ACM Press, Banff (2004)Google Scholar
  9. 9.
    Yousef, M., et al.: Learning from positive examples when the negative class is undetermined- microRNA gene identification. Algorithms Mol. Biol. 3(1), 2 (2008)CrossRefGoogle Scholar
  10. 10.
    AbedAllah, L., Shimshoni, I.: k nearest neighbor using ensemble clustering. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 265–278. Springer, Heidelberg (2012). Scholar
  11. 11.
    Landgrebea, T.C., Paclıka, D.M., Andrew, R.P.: One-Class and Multi-Class Classifier Combining for Ill-Defined Problems. Elsevier Science, Amsterdam (2005)Google Scholar
  12. 12.
    Tax, D.M.J., Duin, R.P.W.: Combining one-class classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 299–308. Springer, Heidelberg (2001). Scholar
  13. 13.
    Lai, C., Tax, D.M.J., Duin, R.P.W., Pękalska, E., Paclík, P.: On combining one-class classifiers for image database retrieval. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 212–221. Springer, Heidelberg (2002). Scholar
  14. 14.
    Juszczak, P., Duin, R.P.W.: Combining one-class classifiers to classify missing data. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 92–101. Springer, Heidelberg (2004). Scholar
  15. 15.
    Ban, T., Abe, S.: Implementing multi-class classifiers by one-class classification methods. In: International Joint Conference on Neural Networks, IJCNN 2006 (2006)Google Scholar
  16. 16.
    Lyu, S., Farid, H.: Steganalysis using color wavelet statistics and one-class support vector machines. In: SPIE Symposium on Electronic Imaging, pp. 35–45 (2004)Google Scholar
  17. 17.
    Menahem, E., Rokach, L., Elovici, Y.: Combining One-Class Classifiers via Meta-learning. CoRR, abs/1112.5246 (2011)Google Scholar
  18. 18.
    Spinosa, E.J., de Carvalho, A.C.P.L.F.: Combining one-class classifiers for robust novelty detection in gene expression data. In: Setubal, J.C., Verjovski-Almeida, S. (eds.) BSB 2005. LNCS, vol. 3594, pp. 54–64. Springer, Heidelberg (2005). Scholar
  19. 19.
    Zhang, J., Lu, J., Zhang, G.: Combining one class classification models for avian influenza outbreaks. In: Computational Intelligence in Multicriteria Decision-Making (MDCM), pp. 190–196. IEEE (2011)Google Scholar
  20. 20.
    Tax, D.M.J.: One-class classification; concept-learning in the absence of counter-examples. Delft University of Technology, June 2001Google Scholar
  21. 21.
    Schölkopf, B., et al.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefGoogle Scholar
  22. 22.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2001)Google Scholar
  23. 23.
    Tax, D.M.J.: DDtools, the Data Description Toolbox for Matlab. Delft University of Technology (2005)Google Scholar
  24. 24.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  25. 25.
    Schölkopf, B., Burges, C.J.C., Smola, A.J.: Advances in Kernel Methods. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  26. 26.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995). Scholar
  27. 27.
    Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405(2), 442–451 (1975)CrossRefGoogle Scholar
  28. 28.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Loai Abedalla
    • 1
    Email author
  • Murad Badarna
    • 3
  • Waleed Khalifa
    • 2
  • Malik Yousef
    • 4
  1. 1.Department of Information SystemsYezreel Valley Academic CollegeYezreel ValleyIsrael
  2. 2.Computer ScienceThe College of SakhninSakhninIsrael
  3. 3.Department of Information SystemsUniversity of HaifaHaifaIsrael
  4. 4.Department of Information SystemsZefat Academic CollegeSafedIsrael

Personalised recommendations