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Dynamic Classifier Integration Method

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Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

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Abstract

The diversity of application domains of pattern recognition makes it difficult to find a highly reliable classification algorithm for sufficiently interesting tasks. In this paper we propose a new combining method, which harness the local confidence of each classifier in the combining process. Our method is at the confluence of two main streams of combining multiple classifiers: classifier fusion and classifier selection. This method learns the local confidence of each classifier using training data and if an unknown data is given, the learned knowledge is used to evaluate the outputs of individual classifiers. An empirical evaluation using five real data sets has shown that this method achieves a promising performance and outperforms the best single classifiers and other known combining methods we tried.

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Author information

Authors and Affiliations

  1. National Computerization Agency, NCA Bldg, 77, Mugyo-dong, Jung-gu, Seoul, 100-775, Korea

    E. Kim

  2. Dept. of CE, Kumoh National Institute of Technology, 1 Yangho-dong, Gumi, Gyeongbuk, 730-701, Korea

    J. Ko

Authors
  1. E. Kim
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  2. J. Ko
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Editor information

Editors and Affiliations

  1. NASA Ames Research Center, Mail Stop 269-1, 94035-1000, Moffett Field, CA, USA

    Nikunj C. Oza

  2. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ, USA

    Robi Polikar

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

    Josef Kittler

  4. University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, E., Ko, J. (2005). Dynamic Classifier Integration Method. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_10

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  • DOI: https://doi.org/10.1007/11494683_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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