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Active Learning Algorithm Using the Discrimination Function of the Base Classifiers

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Image Processing and Communications Challenges 8 (IP&C 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 525))

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Abstract

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.

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References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)

    MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  3. Britto, A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiersa comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)

    Article  Google Scholar 

  4. Burduk, R.: Classifier fusion with interval-valued weights. Pattern Recogn. Lett. 34(14), 1623–1629 (2013)

    Article  Google Scholar 

  5. Choraś, M., Kozik, R.: Machine learning techniques applied to detect cyber attacks on web applications. Logic J. IGPL 23(1), 45–56 (2015)

    Article  MathSciNet  Google Scholar 

  6. Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15(2), 201–221 (1994)

    Google Scholar 

  7. Cyganek, B.: One-class support vector ensembles for image segmentation and classification. J. Math. Imaging Vis. 42(2–3), 103–117 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  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

    Chapter  Google Scholar 

  9. Frank, A., Asuncion, A.: UCI machine learning repository (2010)

    Google Scholar 

  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

    Chapter  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  12. Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recogn. Lett. 22, 25–33 (2001)

    Article  MATH  Google Scholar 

  13. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey (2004)

    Book  MATH  Google Scholar 

  14. Rejer, I.: Genetic algorithm with aggressive mutation for feature selection in BCI feature space. Pattern Anal. Appl. 18(3), 485–492 (2015)

    Article  MathSciNet  Google Scholar 

  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 

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk

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  1. Robert Burduk
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Correspondence to Robert Burduk .

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Editors and Affiliations

  1. Institute of Telecommunications and Computer Sciences, UTP University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. Choraś

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Burduk, R. (2017). Active Learning Algorithm Using the Discrimination Function of the Base Classifiers. In: Choraś, R. (eds) Image Processing and Communications Challenges 8. IP&C 2016. Advances in Intelligent Systems and Computing, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-319-47274-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-47274-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47273-7

  • Online ISBN: 978-3-319-47274-4

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