MLKD logo   Machine Learning &
Knowledge Discovery Group

Publication Details

  Author(s): I. Partalas, G. Tsoumakas, I. Vlahavas.

Title: “Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection”.

Availability: Click here to download the PDF (Acrobat Reader) file (5 pages).

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.

Relevant Links: