Self-Organizing Maps
Learning Vector Quantization with Training Data Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Center-based nearest neighbor classifier
Pattern Recognition
Pattern Recognition Letters
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
Differential Evolution Classifier in Noisy Settings and with Interacting Variables
Applied Soft Computing
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Combining techniques for software quality classification: An integrated decision network approach
Expert Systems with Applications: An International Journal
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Prototype reduction techniques: A comparison among different approaches
Expert Systems with Applications: An International Journal
Distributed learning with data reduction
Transactions on computational collective intelligence IV
International Journal of Applied Mathematics and Computer Science - Semantic Knowledge Engineering
Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Information Sciences: an International Journal
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Evolutionary computation for supervised learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
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The problem addressed in this paper concerns the prototype reduction for a nearest-neighbor classifier. An efficient method based on particle swarm optimization is proposed here for finding a good set of prototypes. Starting from an initial random selection of a small number of training patterns, we generate a set of prototypes, using the particle swarm optimization, which minimizes the error rate on the training set. To improve the classification performance, during the training phase the prototype generation is repeated N times, then each of the resulting N sets of prototypes is used to classify each test pattern, and finally these N classification results are combined by the ''vote rule''. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.