Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
A divide-and-conquer recursive approach for scaling up instance selection algorithms
Data Mining and Knowledge Discovery
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
InstanceRank: Bringing order to datasets
Pattern Recognition Letters
Class Conditional Nearest Neighbor for Large Margin Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel methods for short-term portfolio management
Expert Systems with Applications: An International Journal
Medical data mining: insights from winning two competitions
Data Mining and Knowledge Discovery
Ensemble gene selection for cancer classification
Pattern Recognition
A review of instance selection methods
Artificial Intelligence Review
Fabric defect classification using radial basis function network
Pattern Recognition Letters
Predicting incomplete gene microarray data with the use of supervised learning algorithms
Pattern Recognition Letters
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
Cluster-based instance selection for machine classification
Knowledge and Information Systems
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
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Instance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per class using Borders (instances near to instances belonging to different classes), using this ranking we propose an instance selection algorithm (IRB). We evaluated the proposed algorithm using k-NN with small and large datasets, comparing it against state of the art instance selection algorithms. In our experiments, for large datasets IRB has the best compromise between time and accuracy. We also tested our algorithm using SVM, LWLR and C4.5 classifiers, in all cases the selection computed by our algorithm obtained the best accuracies in average.