this paper, the problem of classifying an unseen
pattern on the basis of its nearest neighbors in a recorded data set
is addressed from the point of view of Dempster-Shafer theory.
Each neighbor of a sample to be classified is considered as an item
of evidence that supports certain hypotheses regarding the class
membership of that pattern. The degree of support is defined as
a function of the distance between the two vectors. The evidence
of the k nearest neighbors is then pooled by means of Dempster's
rule of combination. This approach provides a global treatment
of such issues as ambiguity and distance rejection, and imperfect
knowledge regarding the class membership of training patterns.
The effectiveness of this classification scheme as compared to the
voting and distance-weighted k-NN procedures is demonstrated
using several sets of simulated and real-world data.
