  We showed in this work how the Hassanat distance metric enhances the
performance of the nearest neighbour classifiers. The results demonstrate the
superiority of this distance metric over the traditional and most-used
distances, such as Manhattan distance and Euclidian distance. Moreover, we
proved that the Hassanat distance metric is invariant to data scale, noise and
outliers. Throughout this work, it is clearly notable that both ENN and IINC
performed very well with the distance investigated, as their accuracy increased
significantly by 3.3% and 3.1% respectively, with no significant advantage of
the ENN over the IINC in terms of accuracy. Correspondingly, it can be noted
from our results that there is no optimal algorithm that can solve all
real-life problems perfectly; this is supported by the no-free-lunch theorem
