A New Evidential Distance Measure Based on Belief Intervals

So far, most of the evidential distance and similarity measures proposed in the Dempster-Shafer theory literature have been based on the basic belief assignment function, so as the belief and plausibility functions as two main results of the theory are not directly used in this regard. In this paper, a new evidential distance measure is proposed based on these functions according to nearest neighborhood concept. After assigning basic belief values to propositions and constructing the belief and plausibility functions or the belief interval, this evidential distance measure compares the similarity between the unknown pattern and class belief intervals. For this purpose, we first acquire the belief and plausibility functions or the belief intervals and then the distance between the belief intervals of uncertain pattern feature vectors and samples are calculated. We applied this novel distance measure to the bacillus colonies recognition and coronary heart disease patients classification problems to examine the proposed measure capability in contrast to other evidential measures. Our experiment illustrates that the belief interval distance measure yields the accuracy rates of 91.66 and 92.45 percent for unknown bacillus patterns recognition and coronary heart disease patients classification, respectively, which in contrast to other evidential measures shows superior performance.