Adjusted estimation for the combination of classifiers

Abstract

An algorithm is proposed which adaptively and simultaneously estimates and combines classifiers originating from distinct classification frameworks for improved prediction. The methodology is developed and evaluated on simulations and real data. Analogies and similarities with generalized additive modeling, neural estimation and boosting are discussed. We contrast the approach with existing Bayesian model averaging methods. Areas for further research and development are indicated.