Post-aggregation of classifier ensembles

• Post-aggregation to allow a trainable fusion of massive ensembles is introduced.
• Its effectivenes using GG-FWCs machines is experimentally verified.
• Its relevance when features are not available for all machines is also demonstrated.

We propose to apply an adequate form of an ensemble output to the last level of an additional classifier – the post-aggregation element – as a method to improve ensemble’s performance. Our experimental results prove that a Gate-Generated Functional Weight Classifier post-aggregation serves to get this objective, both in situations in which data are available everywhere and when some features are missing for the post-aggregation task – a case which is relevant for distributed classification problems.Post-aggregation techniques can be especially useful for massive (integrated by many learners) ensembles – such as most the committees, which do not allow trainable first aggregations – and for human decision fusion, because it is unclear what features are considered in this kind of processes.