A Scalable Algorithm for Graph-Based Active Learning

  • Wentao Zhao
  • Jun Long
  • En Zhu
  • Yun Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5059)


In many learning tasks, to obtain labeled instances is hard due to heavy cost while unlabeled instances can be easily collected. Active learners can significantly reduce labeling cost by only selecting the most informative instances for labeling. Graph-based learning methods are popular in machine learning in recent years because of clear mathematical framework and strong performance with suitable models. However, they suffer heavy computation when the whole graph is in huge size. In this paper, we propose a scalable algorithm for graph-based active learning. The proposed method can be described as follows. In the beginning, a backbone graph is constructed instead of the whole graph. Then the instances in the backbone graph are chosen for labeling. Finally, the instances with the maximum expected information gain are sampled repeatedly based on the graph regularization model. The experiments show that the proposed method obtains smaller data utilization and average deficiency than other popular active learners on selected datasets from semi-supervised learning benchmarks.


Active Learning Graph-based Learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hieu, T.N., Arnold, S.: Active learning using pre-clustering. In: Proc. 21th International Conf. on Machine Learning, Banff. Morgan Kaufmann (2004)Google Scholar
  2. 2.
    Muslea, I., Minton, S., Knoblock, C.A.: Active learning with multiple views. Journal of Artificial Intelligence Research 27, 203–233 (2006)MathSciNetGoogle Scholar
  3. 3.
    Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: 17th ACM International Conference on Research and Development in Information Retrieval, pp. 3–12. Springer, Heidelberg (1994)Google Scholar
  4. 4.
    Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. Journal of Machine Learning Research 2, 45–66 (2001)CrossRefGoogle Scholar
  5. 5.
    Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: Proc. 17th International Conf on Machine Learning, pp. 839–846. Morgan Kaufmann, San Francisco (2000)Google Scholar
  6. 6.
    Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. Journal of Artificial Intelligence research 4, 129–145 (1996)MATHGoogle Scholar
  7. 7.
    Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: Proc. 18th International Conf. on Machine Learning, pp. 441–448. Morgan Kaufmann, San Francisco (2001)Google Scholar
  8. 8.
    Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Workshop on Computational Learning Theory, pp. 287–294. Morgan Kaufmann, San Mateo (1992)CrossRefGoogle Scholar
  9. 9.
    Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133–168 (1997)MATHCrossRefGoogle Scholar
  10. 10.
    Abe, N., Mamitsuka, H.: Query learning using boosting and bagging. In: Proc. 15th International Conf. on Machine Learning, Madison, pp. 1–10. Morgan Kaufmann (1998)Google Scholar
  11. 11.
    Melville, P., Mooney, R.J.: Diverse ensembles for active learning. In: Proc. 21th International Conf. on Machine Learning, Banff, pp. 584–591. Morgan Kaufmann (2004)Google Scholar
  12. 12.
    McCallum, A., Nigam, K.: Employing em and pool-based active learning for text classification. In: ICML, pp. 350–358 (1998)Google Scholar
  13. 13.
    Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)Google Scholar
  14. 14.
    Baram, Y., El-Yaniv, R., Luz, K.: Online choice of active learning algorithms. In: ICML, pp. 19–26 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wentao Zhao
    • 1
  • Jun Long
    • 1
  • En Zhu
    • 1
  • Yun Liu
    • 1
  1. 1.National University of Defense TechnologyChangshaChina

Personalised recommendations