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Ensembles of instance selection methods: A comparative study
2019
International Journal of Applied Mathematics and Computer Science
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
doi:10.2478/amcs-2019-0012
fatcat:fv3cqstwd5a7ndodtoztyfvs2y