Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
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
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Graph-Based Discrete Differential Geometry for Critical Instance Filtering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A novel template reduction approach for the K-nearest neighbor method
IEEE Transactions on Neural Networks
Class Conditional Nearest Neighbor for Large Margin Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class boundary preserving algorithm for data condensation
Pattern Recognition
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive nearest neighbor pattern classification
IEEE Transactions on Neural Networks
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Instance-based learning methods often suffer from problems related to high storage requirements, large computational costs for searching through the stored instances to find the ones most similar to the queries, and also sensitivity to noisy samples. In order to deal with these issues, various condensation algorithms have been proposed in the literature to reduce the set of prototypes that need to be stored. In this paper, we propose a new algorithm that uses a set of weights to directly control which prototypes have to be discarded or survive. Instead of relying on indirect heuristics, it explicitly optimizes a bi-objective index which incorporates the condensation rate and a measure of the classification inaccuracy as reflected by the nearest neighbor rule. The proposed algorithm, referred to as DWP (Direct Weighted Pruning), performs an efficient search using a simple genetic algorithm, which is however equipped with three novel acceleration mechanisms to notably speed up its convergence. Experiments over a large number of datasets and comparisons against many other successful condensation algorithms, show that DWP is very effective and achieves the highest classification accuracy along with competitive condensation rates.