Partial Ensemble Classifiers Selection for Better Ranking
Jin Huang, Charles X. Ling · 2006
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 which PECS 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.