Considerations about sample-size sensitivity of a family of edited nearest-neighbor rules

The edited nearest neighbor classification rules constitute a valid alternative to k-NN rules and other nonparametric classifiers. Experimental results with synthetic and real data from various domains and from different researchers and practitioners suggest that some editing algorithms (especially, the optimal ones) are very sensitive to the total number of prototypes considered. This paper investigates the possibility of modifying optimal editing to cope with a broader range of practical situations. Most previously introduced editing algorithms are presented in a unified form and their different properties (acid not just their asymptotic behavior) are intuitively analyzed. The results show the relative limits in the applicability of different editing algorithms.