  K-Nearest neighbor classifier (k-NNC) is simple to use and has little design
time like finding k values in k-nearest neighbor classifier, hence these are
suitable to work with dynamically varying data-sets. There exists some
fundamental improvements over the basic k-NNC, like weighted k-nearest
neighbors classifier (where weights to nearest neighbors are given based on
linear interpolation), using artificially generated training set called
bootstrapped training set, etc. These improvements are orthogonal to space
reduction and classification time reduction techniques, hence can be coupled
with any of them. The paper proposes another improvement to the basic k-NNC
where the weights to nearest neighbors are given based on Gaussian distribution
(instead of linear interpolation as done in weighted k-NNC) which is also
independent of any space reduction and classification time reduction technique.
We formally show that our proposed method is closely related to non-parametric
density estimation using a Gaussian kernel. We experimentally demonstrate using
various standard data-sets that the proposed method is better than the existing
ones in most cases.
