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
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
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Embodied artificial intelligence
Artificial Intelligence
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers in Biology and Medicine
Avoiding Boosting Overfitting by Removing Confusing Samples
ECML '07 Proceedings of the 18th European conference on Machine Learning
The Good, the Bad and the Incorrectly Classified: Profiling Cases for Case-Base Editing
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Scalable Noise Reduction Technique for Large Case-Based Systems
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A divide-and-conquer approach to the pairwise opposite class-nearest neighbor (POC-NN) algorithm
Pattern Recognition Letters
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Reduced Reward-punishment editing for building ensembles of classifiers
Expert Systems with Applications: An International Journal
A new co-training-style random forest for computer aided diagnosis
Journal of Intelligent Information Systems
Tri-training and data editing based semi-supervised clustering algorithm
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
On the use of different classification rules in an editing task
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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
Edited nearest neighbor rule for improving neural networks classifications
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Profiling instances in noise reduction
Knowledge-Based Systems
On the use of data filtering techniques for credit risk prediction with instance-based models
Expert Systems with Applications: An International Journal
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This paper presents new algorithms to identify and eliminate mislabelled, noisy and atypical training samples for supervised learning and more specifically, for nearest neighbour classification. The main goal of these approaches is to enhance the classification accuracy by improving the quality of the training data. Several experiments with synthetic and real data sets are carried out in order to illustrate the behaviour of the schemes proposed here and compare their performance with that of other traditional techniques. It is also analysed the ability of these new algorithms to "reduce" the possible overlapping among regions of different classes.