An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Instance-Based Learning Algorithms
Machine Learning
Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
A new definition of neighborhood of a point in multi-dimensional space
Pattern Recognition Letters
Voting over Multiple Condensed Nearest Neighbors
Artificial Intelligence Review - Special issue on lazy learning
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
On the use of neighbourhood-based non-parametric classifiers
Pattern Recognition Letters - special issue on pattern recognition in practice V
Selection of the optimal prototype subset for 1-NN classification
Pattern Recognition Letters
Data Compression and Local Metrics for Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A Bootstrap Technique for Nearest Neighbor Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Editing Prototypes in the Finite Sample Size Case Using Alternative Neighbourhoods
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Decontamination of Training Samples for Supervised Pattern Recognition Methods
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Filtering (or editing) is mainly effective in improving the classification accuracy of the Nearest Neighbour (NN) rule, and also in reducing its storage and computational requirements. This work reviews some well-known editing algorithms for NN classification and presents alternative approaches based on combining the NN and the Nearest Centroid Neighbourhood of a sample. Finally, an empirical analysis over real data sets is provided.