İnkaya, Tülin2024-08-092024-08-092015-02-010957-4174https://doi.org/10.1016/j.eswa.2014.08.027https://www.sciencedirect.com/science/article/pii/S0957417414005090https://hdl.handle.net/11452/43859In 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.eninfo:eu-repo/semantics/closedAccessNearest-neighbor ruleGraphsBayesClassificationNearest neighborGabriel graphDensityConnectivityScience & technologyTechnologyComputer science, artificial intelligenceEngineering, electrical & electronicOperations research & management scienceComputer scienceEngineeringArticle00034385490001990691242210.1016/j.eswa.2014.08.0271873-6793