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FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class.
An Ensemble Generation Method Based on Instance Hardness
Techniques such as Bagging and Boosting have been successfully applied to a variety of problems.
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats.
