Performance based pruning and weighted voting with classification ensembles

Abstract

Ensemble algorithms have been a very popular research topic because of their high performances. In this work, performance based ensemble pruning and decision weighting methods are investigated on 3 ensemble algorithms (Bagging, Random Subspaces, Random Forest) over 26 classification datasets. According to our experiments; the algorithm including most diversity among its base learners is Random Subspaces. The best performed ensemble algorithm is Random Subspaces with decision weighting.