Publication Details
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Author(s): I. Partalas, G. Tsoumakas, I. Vlahavas.
Title: “Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection”.
Availability:
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Keywords:
ensemble selection.
Appeared in:
18th European Conference on Artificial Intelligence, IOS Press, pp. 117-121, Patras, Greece, 2008. 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. This paper contributes a novel method, based on a new diversity measure that takes into account the strength of the decision of the current ensemble. Experimental comparison of the proposed method, dubbed Focused Ensemble Selection (FES), against state-of-the-art greedy ensemble selection methods shows that it leads to small ensembles with high predictive performance.
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