A density and connectivity based decision rule for pattern classification

Inkaya T.

EXPERT SYSTEMS WITH APPLICATIONS, vol.42, no.2, pp.906-912, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 2
  • Publication Date: 2015
  • Doi Number: 10.1016/j.eswa.2014.08.027
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.906-912
  • Keywords: Classification, Nearest neighbor, Gabriel Graph, Density, Connectivity, NEAREST-NEIGHBOR RULE, GRAPHS, BAYES
  • Bursa Uludag University Affiliated: Yes


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. (C) 2014 Elsevier Ltd. All rights reserved.