  In this paper, the Dempster-Shafer method is employed as the theoretical
basis for creating data classification systems. Testing is carried out using
three popular (multiple attribute) benchmark datasets 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 datasets show extremely high
classification accuracy that is competitive with other popular methods. The
third dataset is non-numerical and difficult to classify, but good results can
be achieved provided the system and mass functions are designed carefully and
the right attributes are chosen for combination. In all cases the
Dempster-Shafer method provides comparable performance to other more popular
algorithms, but the overhead of generating accurate mass functions increases
the complexity with the addition of new attributes. Overall, the results
suggest that the D-S approach provides a suitable framework for the design of
classification systems and that automating the mass function design and
calculation would increase the viability of the algorithm for complex
classification problems.
