Classification algorithm based on pairwise comparison of features

We propose an alternative approach to classification that differs from known approaches 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 related to various aspects of human activity. The results show that the algorithm is competitive compared to known classification algorithms.