Designing neural network committees by combining boosting ensembles

摘要

The use of modified Real Adaboost ensembles by applying weighted emphasis on erroneous and critical (near the classification boundary) has been shown to lead to improved designs, both in performance and in ensemble sizes. In this paper, we propose to take advantage of the diversity among different weighted combination to build committees of modified Real Adaboost designs. Experiments show that the expected improvements are obtained.