Active Learning Algorithm Using the Discrimination Function of the Base Classifiers

  • Robert BurdukEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 525)


The goal of the Active Learning algorithm is to reduce the number of labeled examples needed for learning. In this paper we propose the new AL algorithm based on the analysis of decision profiles. The decision profiles are obtained from the outputs of the base classifiers that form an ensemble of classifiers. The usefulness of the proposed algorithm is experimentally evaluated on several data sets.


Active Learning Query by Committee Multiple classifier system 



This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264.


  1. 1.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)zbMATHGoogle Scholar
  2. 2.
    Borowska, K., Topczewska, M.: New data level approach for imbalanced data classification improvement. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol. 403, pp. 283–294. Springer, Switzerland (2016)CrossRefGoogle Scholar
  3. 3.
    Britto, A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiersa comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)CrossRefGoogle Scholar
  4. 4.
    Burduk, R.: Classifier fusion with interval-valued weights. Pattern Recogn. Lett. 34(14), 1623–1629 (2013)CrossRefGoogle Scholar
  5. 5.
    Choraś, M., Kozik, R.: Machine learning techniques applied to detect cyber attacks on web applications. Logic J. IGPL 23(1), 45–56 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15(2), 201–221 (1994)Google Scholar
  7. 7.
    Cyganek, B.: One-class support vector ensembles for image segmentation and classification. J. Math. Imaging Vis. 42(2–3), 103–117 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Forczmański, P., Łabȩdź, P.: Recognition of occluded faces based on multi-subspace classification. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 148–157. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40925-7_15 CrossRefGoogle Scholar
  9. 9.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
  10. 10.
    Frejlichowski, D.: An algorithm for the automatic analysis of characters located on car license plates. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 774–781. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-39094-4_89 CrossRefGoogle Scholar
  11. 11.
    Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 28(2–3), 133–168 (1997)CrossRefzbMATHGoogle Scholar
  12. 12.
    Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recogn. Lett. 22, 25–33 (2001)CrossRefzbMATHGoogle Scholar
  13. 13.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey (2004)CrossRefzbMATHGoogle Scholar
  14. 14.
    Rejer, I.: Genetic algorithm with aggressive mutation for feature selection in BCI feature space. Pattern Anal. Appl. 18(3), 485–492 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Stefanowski, J., Pachocki, M.: Comparing performance of committee based approaches to active learning. In: Recent Advances in Intelligent Information Systems, pp. 457–470 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations