Limiting the set of neighbors for the k-NCN decision rule: greater speed with preserved classification accuracy

The k nearest centroid neighbor (k-NCN) is a relatively new powerful decision rule based on the concept of so-called surrounding neighborhood. Its main drawback is however slow classification, with complexity O(nk) for classifying a single sample. In this work, we try to alleviate this disadvantage of k-NCN by limiting the set of the candidates for NCN neighbors for a given sample. It is based on an intuitional premise that in most cases, the NCN neighbors are located relatively close to the given sample. During the learning phase, we estimate the fraction of the training set which should be examined only to approximate the "real" k-NCN rule. Experimental results indicate that the accuracy of the original k-NCN may be preserved while the classification costs significantly reduced.