Overfitting in the selection of classifier ensembles

Classifier ensemble selection may be formulated as a learning task since the search algorithm operates by minimizing/maximizing the objective function. As a consequence, the selection process may be prone to overfitting. The objectives of this paper are: (1) to show how overfitting can be detected when the selection is performed by two classical search algorithms: Genetic Algorithm and Particle Swarm Optimization; and (2) to verify which algorithm is more prone to overfitting. The experimental results demonstrate that GA appears to be more affected by overfitting.