Integrated Semi-Supervised Model for Learning and Classification

  • Vandna BhallaEmail author
  • Santanu Chaudhury
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)


Labelled data are not only time consuming but often expensive and difficult to procure as it involves skilful inputs by humans to tag and annotate. Contrary to this unlabelled data is comparatively easier to procure but fewer methods exist to optimally use them. Semi-Supervised Learning overcomes this problem and assists to build better classifiers by using unlabelled data along with sufficient labelled data and may actually yield higher accuracy with considerably less human input effort. But if the labelled data set is inadequate in size then the Semi-Supervised techniques are also stuck. We propose a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model. The model retrains with the unlabelled data to fortify the learning mechanism. We show that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment.


Semi-supervised Self-training Clonal data Integrated 


  1. 1.
    Bhalla, V., Chaudhury, S.: Artificial immune hybrid photo album classifier. In: Proceedings of International Conference on Computer Vision and Image Processing CVIP 2016, vol. 1 (2016)Google Scholar
  2. 2.
    Bhalla, V., Chaudhury, S., Jain, A.: A novel hybrid cnn-ais visual pattern recognition engine, pp. 215–224. Springer International Publishing, Cham (2015)Google Scholar
  3. 3.
    Culp, M., Michailidis, G.: An iterative algorithm for extending learners to a semi-supervised setting. J. Comput. Graph. Stat. 17(3), 545–571 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)Google Scholar
  5. 5.
    Fergus, R., Weiss, Y., Torralba, A.: Semi-supervised learning in gigantic image collections. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems 22, Curran Associates, Inc., pp. 522–530 (2009)Google Scholar
  6. 6.
    Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Getoor, L., Scheffer, T. (eds.) ICML, Omnipress, pp. 513–520 (2011)Google Scholar
  7. 7.
    Jiao, L.C., Shang, F., Wang, F., Liu, Y.: Fast semi-supervised clustering with enhanced spectral embedding. Pattern Recogn. 45(12), 4358–4369 (2012)Google Scholar
  8. 8.
    Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semi-supervised learning with deep generative models. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, Curran Associates, Inc., pp. 3581–3589 (2014)Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    Lee, C.H., Liu, C.L., Hsaio, W.H., Gou, F.S.: Semi-supervised linear discriminant clustering. IEEE Trans. Cybern. 44(7), 9891000 (July 2014)Google Scholar
  11. 11.
    Liu, T., Rosenberg, C., Rowley, H.A.: Clustering billions of images with large scale nearest neighbor search (2007)Google Scholar
  12. 12.
    Maeireizo, B., Litman, D., Hwa, R.: Co-training for predicting emotions with spoken dialogue data. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions (Stroudsburg, PA, USA), ACLdemo ’04, Association for Computational Linguistics (2004)Google Scholar
  13. 13.
    Ororbia II, A.G., Reitter, D., Wu, J., Lee Giles, C.: Online learning of deep hybrid architectures for semi-supervised categorization. In: Machine Learning and Knowledge Discovery in Databases—European Conference, ECML PKDD, Porto, Portugal, September 7–11, 2015. Proceedings, Part I, 2015, pp. 516–532 (2015)Google Scholar
  14. 14.
    Pitelis, N., Russell, C., Agapito,L.: Semi-supervised Learning Using an Unsupervised Atlas, pp. 565–580. Springer, Berlin, Heidelberg (2014)Google Scholar
  15. 15.
    Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4 (Stroudsburg, PA, USA), CONLL ’03, Association for Computational Linguistics, pp. 25–32 (2003)Google Scholar
  16. 16.
    Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models. In: WACV/MOTION, pp. 29–36. IEEE Computer Society (2005)Google Scholar
  17. 17.
    Tuzel, O., Anand, S., Mittal, S., Meer, P.: Semi-supervised kernel mean shift clustering. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1201–1215 (June 2014)Google Scholar
  18. 18.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)Google Scholar
  19. 19.
    Wang, D., Gao, X., Wang, X.: Semi-supervised nonnegative matrix factorization via constraint propagation. IEEE Trans. Cybern. 46(1), 233–244 (2016)CrossRefGoogle Scholar
  20. 20.
    Xiong, S., Azimi, J., Fern, X.Z.: Active learning of constraints for semi-supervised clustering. IEEE Trans. Knowl. Data Eng. 26(1), 43–54 (2013)CrossRefGoogle Scholar
  21. 21.
    Zeng, H., Cheung, Y.-M.: Semi-supervised maximum margin clustering with pairwise constraints. IEEE Trans. Knowl. Data Eng. 24(5), 926–939 (2012)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Tian, G., Mu, Y., Fan, W.: Supervised deep learning with auxiliary networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA), KDD ’14, pp. 353–361. ACM (2014)Google Scholar
  23. 23.
    Zheng, L., Li, T.: Semi-supervised hierarchical clustering. In: Proceedings of the IEEE 11th International Conference on Data Mining, p. 982991 (2011)Google Scholar
  24. 24.
    Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison (2005)Google Scholar
  25. 25.
    Zhu, X., Goldberg, A.B., Brachman, R., Dietterich, T.: Introduction to Semi-supervised Learning. Morgan and Claypool Publishers (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Indian Institute of Technology DelhiHauz KhasIndia
  2. 2.CEERI PILANIPilaniIndia

Personalised recommendations