Ensemble learning is a system that improves the performance and robustness of the classification problems. How to combine the outputs of base classifiers is one of the fundamental challenges in ensemble learning systems. In this paper, an optimized Static Ensemble Selection (SES) approach is first proposed on the basis of NSGA-II multi-objective genetic algorithm (called SES-NSGAII), which selects the best classifiers along with their combiner, by simultaneous optimization of error and diversity objectives. In the second phase, the Dynamic Ensemble Selection-Performance (DES-P) is improved by utilizing the first proposed method. The second proposed method is a hybrid methodology that exploits the abilities of both SES and DES approaches and is named Improved DES-P (IDES-P). Accordingly, combining static and dynamic ensemble strategies as well as utilizing NSGA-II are the main contributions of this research. Findings of the present study confirm that the proposed methods outperform the other ensemble approaches over 14 datasets in terms of classification accuracy. Furthermore, the experimental results are described from the view point of Pareto front with the aim of illustrating the relationship between diversity and the over-fitting problem. 
A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches