Post-aggregation of classifier ensembles

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. (C) 2015 Elsevier B.V. All rights reserved.

Post-aggregation of classifier ensembles Articles