Relearning ensemble selection based on new generated features
This addresses the problem of optimizing ensemble classifier selection for machine learning practitioners, though it appears incremental.
The authors tackled the problem of improving ensemble classification effectiveness by proposing a framework that selects classifiers with relearning and uses newly generated features from that process. They demonstrated competitive performance against state-of-the-art methods on three benchmark datasets and one synthetic dataset using four classification measures.
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.