The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification
tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the
k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification
rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based
on these new rules have been tested and compared.