Bagging Improves Uncertainty Representation In Evidential Pattern Classification

Uncertainty representation is a major issue in pattern recognition when the outputs of a classi er do not lead directly to a nal decision, but are used in combination with other systems, or as input to an interactive decision process. In such contexts, it may be advantageous to resort to rich and exible formalisms for representing and manipulating uncertain information, such as the Dempster-Shafer theory of Evidence. In this paper, it is shown that the quality and reliability of the outputs from an evidence-theoretic classi er may be improved using an adaptation from a resample-and-combine approach introduced by Breiman and known as "bagging". This approach is explained and studied experimentally using simulated data. In particular, results show that bagging improves classi cation accuracy and limits the inuence of outliers and ambiguous training patterns