Computational Biology and Chemistry
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Letters: Adaptive local hyperplane classification
Neurocomputing
Expert Systems with Applications: An International Journal
Particle swarm optimization for prototype reduction
Neurocomputing
Cluster-based nearest-neighbour classifier and its application on the lightning classification
Journal of Computer Science and Technology
Engineering Applications of Artificial Intelligence
Discriminant feature extraction based on center distance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel classifier based on shortest feature line segment
Pattern Recognition Letters
Prototype reduction techniques: A comparison among different approaches
Expert Systems with Applications: An International Journal
Perceptual relativity-based local hyperplane classification
Neurocomputing
Coarse to fine K nearest neighbor classifier
Pattern Recognition Letters
Pattern Recognition Letters
ATISA: Adaptive Threshold-based Instance Selection Algorithm
Expert Systems with Applications: An International Journal
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In this paper, a novel center-based nearest neighbor (CNN) classifier is proposed to deal with the pattern classification problems. Unlike nearest feature line (NFL) method, CNN considers the line passing through a sample point with known label and the center of the sample class. This line is called the center-based line (CL). These lines seem to have more capacity of representation for sample classes than the original samples and thus can capture more information. Similar to NFL, CNN is based on the nearest distance from an unknown sample point to a certain CL for classification. As a result, the computation time of CNN can be shortened dramatically with less accuracy decrease when compared with NFL. The performance of CNN is demonstrated in one simulation experiment from computational biology and high classification accuracy has been achieved in the leave-one-out test. The comparisons with nearest neighbor (NN) classifier and NFL classifier indicate that this novel classifier achieves competitive performance.