Proceedings of the third international conference on Genetic algorithms
GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Combining Control Strategies Using Genetic Algorithms with Memory
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
A selective sampling approach to active feature selection
Artificial Intelligence
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
A case study of memetic algorithms for constraint optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
A divide-and-conquer recursive approach for scaling up instance selection algorithms
Data Mining and Knowledge Discovery
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Prototype selection algorithms for distributed learning
Pattern Recognition
Text Mining: Predictive Methods for Analyzing Unstructured Information
Text Mining: Predictive Methods for Analyzing Unstructured Information
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
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Although many more complex learning algorithms exist, k-nearest neighbor is still one of the most successful classifiers in real-world applications. One of the ways of scaling up the k-nearest neighbors classifier to deal with large datasets is instance selection. Due to the constantly growing amount of data in almost any pattern recognition task, we need more efficient instance selection algorithms, which must achieve larger reductions while maintaining the accuracy of the selected subset. In this paper we present a way to improve instance selection by allowing the algorithms to select instances more than once. In this way, fewer instances can cover more portions of the space, and the same testing accuracy of the standard approach can be obtained faster and with fewer instances. Although the approach is general enough to be used in any instance selection algorithm, we focus on evolutionary instance selection due to its superior performance. An extensive comparison using 45 datasets from the UCI Machine Learning Repository shows the usefulness of our approach compared with the established method of evolutionary instance selection. Our method is able to, in the worst case, match the accuracy obtained by standard instance selection with a larger reduction and shorter execution time. In addition, the method is applied to class-imbalance problems with very good results.