Single and Multi-Objective Genetic Algorithms for the Selection of Ensemble of Classifiers

Many recent works have investigated methods to select subsets of classifiers instead of combining all available classifiers. The majority of these works has concluded that the combiner error rate is better than diversity to guide the selection process in order to identify the best performing subset of classifiers. However, the classifier selection process has to take into account three different aspects: complexity, overfitting and performance. These aspects of the selection process have not yet been tackled simultaneously in the literature. The study presented in this paper, deals with these three aspects in a handwritten digit recognition problem. Different search criteria such as diversity, error rate and number of classifiers are applied in single and multi-objective optimization approaches using genetic algorithms. In our experiments, we observed that error rate applied in a single optimization approach was the best objective function to increase performance. The generalized diversity and interrater agreement measures, combined with error rate in pairs of objective functions were the best measures to reduce complexity and keep good performance in a multi-objective optimization approach. Finally, the performance of the solutions found in both, single and multi-objective optimization processes were increased by applying a global validation method to reduce overfitting.