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Title: A Study on Greedy Algorithms for Ensemble Pruning
Author(s): I. Partalas, G. Tsoumakas, I. Vlahavas.
Tech. Report: TR-LPIS-360-12, LPIS Group, Dept. of Informatics, Aristotle University of Thessaloniki, Greece, 2012
Availability: Click here to download the PDF (Acrobat Reader) file.
Abstract: Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. They use different directions for searching this space and different mea- sures for evaluating the available actions at each state. Some use the training set for subset evaluation, while others a separate validation set. This paper abstracts the key points of these methods and offers a general framework of the greedy ensemble selection algorithm, discussing its important parameters and the different options for instantiating these parameters.
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