Abstract
Ensemble learning is a popular strategy to take advantage of classification techniques with distinct biases, by joining their competences and capabilities. This work will investigate strategies to generate ensembles of classifiers based on their competences as evaluated by a meta-learning strategy. Nonetheless, joining different classifiers is profitable only when their errors are uncorre…