K Nearest Neighbor Equality: Giving equal chance to all existing classes

The nearest neighbor classification method assigns an unclassified point to the class of the nearest case of a set of previously classified points.This rule is independent of the underlying joint distribution of the sample points and their classifications.An extension to this approach is the k-NN method, in which the classification of the unclassified point is made by following a voting criteria within the k nearest points.The method we present here extends the k-NN idea, searching in each class for the k nearest points to the unclassified point, and classifying it in the class which minimizes the mean distance between the unclassified point and the k nearest points within each class.As all classes can take part in the final selection process, we have called the new approach k Nearest Neighbor Equality (k-NNE).The experimental results we obtained empirically show the suitabulity of the k-NNE algorithm, and its effectiveness suggests that it could be added to the current list of distance based classifiers