  This paper proposes a new distance metric function, called Z distance, for
KNN classification. The Z distance function is not a geometric direct-line
distance between two data points. It gives a consideration to the class
attribute of a training dataset when measuring the affinity between data
points. Concretely speaking, the Z distance of two data points includes their
class center distance and real distance. And its shape looks like "Z". In this
way, the affinity of two data points in the same class is always stronger than
that in different classes. Or, the intraclass data points are always closer
than those interclass data points. We evaluated the Z distance with
experiments, and demonstrated that the proposed distance function achieved
better performance in KNN classification.
