Ensemble learning based on random super-reduct and resampling

Abstract

Ensemble learning has been widely used for improving the performance of base classifiers. Diversity among base classifiers is considered as a key issue in ensemble learning. Recently, to promote the diversity of base classifiers, ensemble methods through multi-modal perturbation have been proposed. These methods simultaneously use two or more perturbation techniques when generating base classifiers. In this paper, from the perspective of multi-modal perturbation, we propose an ensemble approach (called ‘E $$\_$$ RSRR’) based on random super-reduct and resampling. To generate a set of accurate and diverse base classifiers, E $$\_$$ RSRR adopts a new multi-modal perturbation strategy. This strategy combines two perturbation techniques together, that is, resampling and random super-reduct. First, it perturbs the sample space via the resampling technique; Second, it perturbs the feature space via the random super-reduct technique, which is a combination of RSS (random subspace selection) technique and ADEFS (approximate decision entropy-based feature selection) method in rough sets. Experimental results show that E $$\_$$ RSRR can provide competitive solutions for ensemble learning.