Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
ACM Computing Surveys (CSUR)
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Self-Organizing Maps
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
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
Adapting k-means for supervised clustering
Applied Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Nearest neighbor editing aided by unlabeled data
Information Sciences: an International Journal
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Data characterization for effective prototype selection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
An algorithm for a selective nearest neighbor decision rule (Corresp.)
IEEE Transactions on Information Theory
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
On the use of meta-learning for instance selection: An architecture and an experimental study
Information Sciences: an International Journal
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Although there are several proposals in the instance selection field, none of them consistently outperforms the others over a wide range of domains. In recent years many authors have come to the conclusion that data must be characterized in order to apply the most suitable selection criterion in each case. In light of this hypothesis, herein we propose a set of measures to characterize databases. These measures were used in decision rules which, given their values for a database, select from some pre-selected methods, the method, or combination of methods, that is expected to produce the best results. The rules were extracted based on an empirical analysis of the behaviors of several methods on several data sets, then integrated into an algorithm which was experimentally evaluated over 20 databases and with six different learning paradigms. The results were compared with those of five well-known state-of-the-art methods.