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Title: An Ensemble Pruning Primer
Author(s): G. Tsoumakas, I. Partalas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (13 pages).
Appeared in: Supervised and Unsupervised Methods and Their Applications to Ensemble Methods (SUEMA 2009), Oleg Okun and Giorgio Valentini (Eds.), Springer Verlag, Volume 245/2009, pp. 1-13, 2009.
Abstract: Ensemble pruning deals with the reduction of an ensemble of predictive models in order to improve its e±ciency and predictive performance. The last 12 years a large number of ensemble pruning methods have been proposed. This work proposes a taxonomy for their organization and reviews important representative methods of each category. It abstracts their key components and discusses their main advantages and disadvantages.We hope that this work will serve as a good starting point and reference for researchers working on the development of new ensemble pruning methods.
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