Dynamic ensemble re-construction for better ranking

Authors:
Jin Huang;Charles X. Ling
Affiliations:
Department of Computer Science, The University of Western Ontario, London, Ontario, Canada;Department of Computer Science, The University of Western Ontario, London, Ontario, Canada
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

Ensemble learning has been shown to be very successful in data mining. However most work on ensemble learning concerns the task of classification. Little work has been done to construct ensembles that aim to improve ranking. In this paper, we propose an approach to re-construct new ensembles based on a given ensemble with the purpose to improve the ranking performance, which is crucial in many data mining tasks. The experiments with real-world data sets show that our new approach achieves significant improvements in ranking over the original Bagging and Adaboost ensembles.