Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection

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.

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Ioannis Partalas, Grigorios Tsoumakas, Ioannis P. Vlahavas
• Keywords: Artificial Intelligence, ECAI 2008, Ensemble Selection, Ensemble Selection Deals, Ensemble Selection Methods