Abstract—We propose an alternative approach to classification that differs from known ap-
proaches in that instead of comparing the tuple of values of a test object’s features with similar
tuples of features for objects in the training set, in this approach we make independent pairwise
comparisons of every pair of feature values for the objects being compared. Here instead of
using the notion of a “nearest neighbors” for test object, we introduce the notion of “admissible
proximity” for each feature value in the test object. In this approach, we propose an alternative
algorithm for classification that has a number of significant practical features. The algorithm’s
quality was evaluated on sample problems taken from the well-known UCI repository and re-
lated to various aspects of human activity. The results show that the algorithm is competitive
compared to known classification algorithms.