Evolving ensembles by boosting

This paper presents evolving ensembles by boosting (EEB) for designing NN ensembles by combining evolutionary learning and boosting algorithm and negative correlation learning (NCL). Its advantages include: first, the use of NCL is to encourage different individual networks in the ensemble to learn different parts of the training data. The individual networks are trained simultaneously. This provides an opportunity for the individual networks to interact with each other and to specialize; Second, there are two levels of interaction, one of which is caused by NCL; the other is caused by using the weight update scheme similar to that in boosting algorithm to form the final combination. So, in these senses the proposed algorithm EEB learns and combines individual networks exactly in the same process. Third, unlike all the other algorithms, our selection mechanism is based on the dynamic weight vector updated by boosting that can fine-tune the contributions of individual networks made to the whole ensemble.