A comparison between fuzzy and evidence - theoretic k - NN rules

Recently, a new pattern classiier using neighborhood information in the framework of the Dempster-Shafer theory of evidence was introduced 3, 2]. This approach consists in considering each neighbor of a pattern to be classiied 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 deene the basic probability assignments are learnt from the data by minimizing the mean squared error between the classiier's outputs and target values. Several sets of artiicial and real-world data are used for comparison with the voting and fuzzy k-nearest neighbor rules.