A density and connectivity based decision rule for pattern classification

•Surrounding Influence Region (SIR) decision rule is proposed for pattern classification.•SIR decision rule is a parameter-free approach.•Gabriel Graph is used to define the proximity, connectivity and density relations among the data points.•A unique neighborhood is constructed for each sample point.•SIR decision rule is superior to the k-NN and GGN decision rules in artificial and real data sets.

In this paper we propose a novel neighborhood classifier, Surrounding Influence Region (SIR) decision rule. Traditional Nearest Neighbor (NN) classifier is a distance-based method, and it classifies a sample using a predefined number of neighbors. In this study neighbors of a sample are determined using not only the distance, but also the connectivity and density information. One of the well-known proximity graphs, Gabriel Graph, is used for this purpose. The neighborhood is unique for each sample. SIR decision rule is a parameter-free approach. Our experiments with artificial and real data sets show that the performance of the SIR decision rule is superior to the k-NN and Gabriel Graph neighbor (GGN) classifiers in most of the data sets.