Overfitting in the selection of classifier ensembles: A comparative study between PSO and GA
Overfitting in the selection of classifier ensembles: A comparative study between PSO and GA
Dos Santos, Eulanda M. and Sabourin, Robert and Oliveira, Luiz S. and Maupin, Patrick
GECCO’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 2008
Abstract : 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.
