Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Colour image segmentation and labeling through multiedit-condensing
Pattern Recognition Letters
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Enhancing Density-Based Data Reduction Using Entropy
Neural Computation
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
A class boundary preserving algorithm for data condensation
Pattern Recognition
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Noisy data elimination using mutual k-nearest neighbor for classification mining
Journal of Systems and Software
K Nearest Neighbor Equality: Giving equal chance to all existing classes
Information Sciences: an International Journal
Arabic handwriting recognition using structural and syntactic pattern attributes
Pattern Recognition
A hybrid KNN-ant colony optimization algorithm for prototype selection
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
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The K-nearest neighbor (KNN) rule is one of the most widely used pattern classification algorithms. For large data sets, the computational demands for classifying patterns using KNN can be prohibitive. A way to alleviate this problem is through the condensing approach. This means we remove patterns that are more of a computational burden but do not contribute to better classification accuracy. In this brief, we propose a new condensing algorithm. The proposed idea is based on defining the so-called chain. This is a sequence of nearest neighbors from alternating classes. We make the point that patterns further down the chain are close to the classification boundary and based on that we set a cutoff for the patterns we keep in the training set. Experiments show that the proposed approach effectively reduces the number of prototypes while maintaining the same level of classification accuracy as the traditional KNN. Moreover, it is a simple and a fast condensing algorithm.