Ensemble Learning for Free with Evolutionary Algorithms ?
Christian Gagné, Michele Sebag, Marc Schoenauer and Marco Tomassini
Evolutionary Learning (EL) proceeds by evolving a population of classifiers, and it most often (with some notable exceptions) returns the single best-of-run classifier. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers.
Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions.
A new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is
used to extract the classifier ensemble from the final population only (Off-EEL) or incrementally along evolution (On-EEL).
Experiments on a set of benchmark problems show that Off-EEL outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.