Publication:
A density and connectivity based decision rule for pattern classification

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2015-02-01

Authors

İnkaya, Tülin

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Elsevier

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Abstract

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.

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Keywords

Nearest-neighbor rule, Graphs, Bayes, Classification, Nearest neighbor, Gabriel graph, Density, Connectivity, Science & technology, Technology, Computer science, artificial intelligence, Engineering, electrical & electronic, Operations research & management science, Computer science, Engineering

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