  Ensembles of artificial neural networks show improved generalization
capabilities that outperform those of single networks. However, for aggregation
to be effective, the individual networks must be as accurate and diverse as
possible. An important problem is, then, how to tune the aggregate members in
order to have an optimal compromise between these two conflicting conditions.
We present here an extensive evaluation of several algorithms for ensemble
construction, including new proposals and comparing them with standard methods
in the literature. We also discuss a potential problem with sequential
aggregation algorithms: the non-frequent but damaging selection through their
heuristics of particularly bad ensemble members. We introduce modified
algorithms that cope with this problem by allowing individual weighting of
aggregate members. Our algorithms and their weighted modifications are
favorably tested against other methods in the literature, producing a sensible
improvement in performance on most of the standard statistical databases used
as benchmarks.
