Post-aggregation of classifier ensembles

Abstract

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.