Journal article

Data classification using the Dempster–Shafer method

Qi Chen, Amanda Whitbrook, Uwe Aickelin, Chris Roadknight

Journal of Experimental and Theoretical Artificial Intelligence | Taylor & Francis | Published : 2014

Abstract

In this paper, the Dempster–Shafer (D–S) method is used as the theoretical basis for creating data classification systems. Testing is carried out using three popular multiple attribute benchmark data-sets that have two, three and four classes. In each case, a subset of the available data is used for training to establish thresholds, limits or likelihoods of class membership for each attribute, and hence create mass functions that establish probability of class membership for each attribute of the test data. Classification of each data item is achieved by combination of these probabilities via Dempster's rule of combination. Results for the first two data-sets show extremely high classificati..

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University of Melbourne Researchers