  The ensemble methods are meta-algorithms that combine several base machine
learning techniques to increase the effectiveness of the classification. Many
existing committees of classifiers use the classifier selection process to
determine the optimal set of base classifiers. In this article, we propose the
classifiers selection framework with relearning base classifiers. Additionally,
we use in the proposed framework the new generated feature, which can be
obtained after the relearning process. The proposed technique was compared with
state-of-the-art ensemble methods using three benchmark datasets and one
synthetic dataset. Four classification performance measures are used to
evaluate the proposed method.
