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Metaheuristic Ensemble Pruning via Greedy-Based Optimization Selection
Abstract
Ensemble selection is a crucial problem for ensemble learning (EL) to speed up the predictive model, reduce the storage space requirements and to further improve prediction accuracy. Diversity among individual predictors is widely recognized as a key factor to successful ensemble selection (ES), while the ultimate goal of ES is to improve its predictive accuracy and generalization of the ensemble. Motivated by the problems stated in previous, we have devised a novel hybrid layered based greedy ensemble reduction (HLGER) architecture to delete the predictor with lowest accuracy and diversity with evaluation function according to the diversity metrics. Experimental investigations are conducted based on benchmark time series data sets, support vectors regression algorithm utilized as base learner to generate homogeneous ensemble, HLGER uses locally weight ensemble (LWE) strategies to provide a final ensemble prediction. The experimental results demonstrate that, in comparison with benchmark ensemble pruning techniques, HLGER achieves significantly superior generalization performance.
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