Improving Classifiers and Regions of Competence in Dynamic Ensemble Selection

This paper evaluates some strategies to approximate the performance of dynamic ensembles based on NN-rule to the oracle performance. For this purpose, we use a multi-objective optimization algorithm, based on Differential Evolution, to generate automatically a pool of accurate and diverse classifiers in the form of Extreme Learning Machines. However, the rule defined for selecting the classifiers depends on the quality of the information obtained from regions of competence. Thus, we also improve the regions of competence in order to avoid noise and create smoother class boundaries. Finally, we employ a dynamic ensemble selection method. The performance of the proposed method was experimentally investigated using 12 benchmark datasets and results of comparative analysis are presented.