Designing a Multilayer Feedforward Ensemble with the Weighted Conservative Boosting Algorithm

In previous researches we have analysed some methods to create committees of multilayer feedforward networks trained with the backpropagation algorithm. One of the most known methods that we have studied is Adaptive Boosting. In this paper we propose a variation of this method called weighted conservative boosting based on conservative boosting. In this case, a weight which depends on the database and on the ensemble is added to the equation used to update the sampling distribution. We have tested adaptive boosting, conservative boosting and weighted conservative boosting with seven databases from the UCI repository. We have used the mean Increase of Performance and the mean percentage of error reduction to compare both methods, the results show that weighted conservative boosting is the best performing method.