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Feature Based Decision Fusion

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Advances in Pattern Recognition — ICAPR 2001 (ICAPR 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2013))

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Abstract

In this paper we present a new architecture for combining classifiers. This approach integrates learning into the voting scheme used to aggregate individual classifiers decisions. This overcomes the drawbacks of having static voting techniques. The focus of this work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. Test results show improvement in the overall classification rates over any individual classifier, as well as different static classifier-combining schemes.

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Author information

Authors and Affiliations

  1. Pattern Analysis and Machine Intelligence lab, Department of Systems Design Engineering, University ofWaterloo, N2L-3G1, Waterloo, Ontario, Canada

    Nayer M. Wanas & Mohamed S. Kamel

Authors
  1. Nayer M. Wanas
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  2. Mohamed S. Kamel
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, EX4 4PT, Exeter, UK

    Sameer Singh

  2. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  3. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Favoritenstr. 9/2/4, 1040, Wien, Austria

    Walter Kropatsch

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© 2001 Springer-Verlag Berlin Heidelberg

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Wanas, N.M., Kamel, M.S. (2001). Feature Based Decision Fusion. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_18

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  • DOI: https://doi.org/10.1007/3-540-44732-6_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41767-5

  • Online ISBN: 978-3-540-44732-0

  • eBook Packages: Springer Book Archive

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