A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches
Proposing a new hybrid approach for ensemble learning systems that exploits the abilities of static ensemble selection (SES) and dynamic ensemble selection (DES) strategies. Presenting an SES approach based on NSGAII multi-objective genetic algorithm. Improving one of the DES approaches by utilizing the SES proposed method. Justifying the performance of the proposed methods by UCI repository and LKC datasets.
ensemble learning system static ensemble selection dynamic ensemble selection classifier combination classifier diversity multi-objective optimization