On Evolutionary Classification Ensembles

Ensemble methods train multiple classifiers and combine their outcomes to improve the generalization ability of a learning system. Although there exist methods for automatic creation of ensembles, they require heavy fine-tuning and the impact of their hyper-parameters on the optimization process very often remains unknown. In this paper, we propose a genetic algorithm for evolving classification ensembles. It is coupled with a pre-processing routine which deals with potential data imbalance and redundancy within the training and/or feature sets. Our experimental study, performed over binary and multi-class benchmark datasets and backed up with statistical analysis, revealed that the proposed technique outperforms other algorithms for building multiple classifier systems. It also helped understand the impact of various (hyper-)parameters of our method on its exploration/exploitation capabilities.