Dynamic Feature Selection for Classifier Ensembles

In this paper, we propose a novel approach for dynamic feature selection to be applied in classifier ensembles. This method selects the best attribute subsets for an individual instance or a group of instances of an input dataset. Hence, each testing instance is classified using a unique feature subset in the classification process. The main aim of this paper is to extend a dynamic feature selection method that was proposed for single classifiers, adjusting this approach to be used in classifier ensembles. In order to validate our proposed method, an empirical analysis is conducted to investigate the effectiveness of approach compared to existing ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed method to the existing ensemble methods.