Hybrid Optimization Method Applied to Adaptive Splitting and Selection Algorithm

The paper presents an approach to train combined classifiers based on feature space splitting and selection of the best classifier ensemble to each subspace of feature space. The learning method uses a hybrid algorithm that combines a Genetic Algorithm and Cross Entropy Method. The proposed approach was evaluated on the basis of the comprehensive computer experiments run on balanced and imbalanced datasets, and compared with Cluster and Selection algorithm, improving the results obtained by this technique