Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Lazy learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
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
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
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
A review of instance selection methods
Artificial Intelligence Review
Information Sciences: an International Journal
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Instance selection is a feasible strategy to solve the problem of dealing with large databases in inductive learning. There are several proposals in this area, but none of them consistently outperforms the others over a wide range of domains. In this paper we present a set of measures to characterize the databases, as well as a new algorithm that uses these measures and, depending on the data characteristics, it applies the method or combination of methods expected to produce the best results. This approach was evaluated over 20 databases and with six different learning paradigms. The results have been compared with those achieved by five well-known state-of-the-art methods.