Ensembles of instance selection methods: A comparative study - Blachnik, Marcin (Zielona Góra: Uniwersytet Zielonogórski)
Instance selection is often performed as one of the preprocessing methods which, along with feature selection, allows a significant reduction in computational complexity and an increase in prediction accuracy. So far, only few authors have considered ensembles of instance selection methods, while the ensembles of final predictive models attract many researchers. To bridge that gap, in this paper we compare four ensembles adapted to instance selection: "Bagging", "Feature Bagging", "AdaBoost" and "Additive Noise". The last one is introduced for the first time in this paper.
Keywords: machine learning; classification; instance selection; ensemble methods