Towards decision rule based on closer symmetric neighborhood

The relatively new k Nearest Centroid Neighbor (k-NCN) decision rule uses an interesting concept of surrounding neighborhood, that is, such a neighborhood which takes into account not only the proximity of neighbors but also their spatial location. In the paper we propose a new decision rule, called k Near Surrounding Neighbors (k-NSN), which "improves" the neighborhood used in k-NCN with respect to both mentioned aspect. We tested several methods, k-NN included, each in a multidecision and in two binary decomposition schemes, on a few UCI datasets and a large ferrite core dataset, to show attractiveness of the presented concept in applications where the prediction accuracy is of utmost importance.