Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of new techniques to obtain quality training sets
Pattern Recognition Letters - Special issue: Sibgrapi 2001
On Filtering the Training Prototypes in Nearest Neighbour Classification
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
A novel gray-based reduced NN classification method
Pattern Recognition
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
A Margin Maximization Training Algorithm for BP Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Pseudo nearest neighbor rule for pattern classification
Expert Systems with Applications: An International Journal
A divide-and-conquer approach to the pairwise opposite class-nearest neighbor (POC-NN) algorithm
Pattern Recognition Letters
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A stochastic approach to wilson's editing algorithm
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
New rank methods for reducing the size of the training set using the nearest neighbor rule
Pattern Recognition Letters
Improving nearest neighbor classification with simulated gravitational collapse
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A training sample sequence planning method for pattern recognition problems
Automatica (Journal of IFAC)
An improved fast edit approach for two-string approximated mean computation applied to OCR
Pattern Recognition Letters
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The edited nearest neighbor classification rules constitute a valid alternative to k-NN rules and other nonparametric classifiers. Experimental results with synthetic and real data from various domains and from different researchers and practitioners suggest that some editing algorithms (especially, the optimal ones) are very sensitive to the total number of prototypes considered. This paper investigates the possibility of modifying optimal editing to cope with a broader range of practical situations. Most previously introduced editing algorithms are presented in a unified form and their different properties (acid not just their asymptotic behavior) are intuitively analyzed. The results show the relative limits in the applicability of different editing algorithms