Partial Ensemble Classifiers Selection for Better Ranking

Authors:
Jin Huang;Charles X. Ling
Affiliations:
University of Western Ontario;University of Western Ontario
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

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Abstract

Ranking is an important task in data mining and knowledge discovery. We propose a novel approach called PECS algorithm to improve the overall ranking performance of a given ensemble. We formally analyse the sufficient and necessary condition under whichPECS algorithm can effectively improve ensemble ranking performance. The experiments with real-world data sets show that this new approach achieves significant improvements in ranking over the original Bagging and Adaboost ensembles.