Analysis of evidence-theoretic decision rules for pattern classification

The Dempster-Shafer theory provides a convenient framework for decision making based on very limited or weak information. Such situations typically arise in pattern recognition problems when patterns have to be classified based on a small number of training vectors, or when the training set does not contain samples from all classes. This paper examines different strategies that can be applied in this context to reach a decision (e.g. assignment to a class or rejection), provided the possible consequences of each action can be quantified. The corresponding decision rules are analysed under different assumptions concerning the completeness of the training set. These approaches are then demonstrated using real data.

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