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Using Dempster-Shafer Theory in MCF Systems to Reject Samples

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Book cover Multiple Classifier Systems (MCS 2005)

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

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Abstract

In this paper the Dempster-Shafer theory of evidence is utilised in multiple classifier systems to define rejection criteria for samples presented for classification. The DS theory offers the possibility to derive a measure of contradiction between the classifier decisions to be fused. Moreover, assigning positive belief mass to the universal hypothesis Θ in the basic probability assignments produced by the classifiers, allows to quantify the belief in their correctness. Both criteria have been evaluated by numerical simulations on two different benchmark data sets. The results are compared to standard static classifier combination schemes and basic classifiers. It is shown that DS classifier fusion can boost the combined classifier accuracy to 100% on the set of accepted data points (~ 70%). This behaviour could be of interest in applications with high costs for a miss, e.g. in medical screening tests.

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Author information

Authors and Affiliations

  1. Department of Neural Information Processing, Universität Ulm, 89069, Ulm, Germany

    Christian Thiel, Friedhelm Schwenker & Günther Palm

Authors
  1. Christian Thiel
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  2. Friedhelm Schwenker
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  3. Günther Palm
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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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Thiel, C., Schwenker, F., Palm, G. (2005). Using Dempster-Shafer Theory in MCF Systems to Reject Samples. 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_12

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  • DOI: https://doi.org/10.1007/11494683_12

  • 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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