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.