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A density and connectivity based decision rule for pattern classification
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DOI：10.1016/j.eswa.2014.08.027.png)
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Abstract
En 中文
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.
Keywords:
Classification
Nearest neighbor
Gabriel Graph
Density
Connectivity
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