Evolving ensemble of classifiers in random subspace
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souza Britto · 2006
Various methods for ensemble selection and classifier combination have been designed to optimize the results of ensembles of classifiers. Genetic algorithm (GA) which uses the diversity for the ensemble selection could be very time consuming. We propose compound diversity functions as objective functions for a faster and more effective GA searching. Classifiers selected by GA are combined by a proposed pairwise confusion matrix transformation, which offer strong performance boost for EoCs.