Abstract
A new classifier using neighborhood information in the framework of the Dempster-Shafer theory of evidence has recently been introduced. This approach consists in considering each neighbor of a pattern to be classified as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. In this paper, an adaptive version of this method is proposed, in which the parameters used to define the basic probability assignments are learnt from the data by minimizing the mean squared error between the classifier outputs and target values. Based on the evidence-theoretic concepts of degree of conflict and ignorance, new reject rules are introduced. Several sets of artificial and real-world data are used for comparison with the voting and distanceweighted classifiers.
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© 1995 Springer-Verlag Berlin Heidelberg
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Zouhal, L.M., Denœux, T. (1995). An adaptive k-NN rule based on Dempster-Shafer theory. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_311
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DOI: https://doi.org/10.1007/3-540-60268-2_311
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