Constructing Ensembles for Better Ranking

We propose a novel algorithm, RankDE, to build an ensemble using an extra artificial dataset. RankDE aims at improving the overall ranking performance, which is crucial in many machine learning applications. This algorithm constructs artificial datasets that are diverse with the current training dataset in terms of ranking. We conduct experiments with real-world data sets to compare RankDE with some traditional and state-of-the-art ensembling algorithms of Bagging, Adaboost, DECORATE and Rankboost in terms of ranking. The experiments show that RankDE outperforms Bagging, DECORATE, Adaboost, and Rankboost when limited data is available. When enough training data is available, it is competitive with DECORATE and Adaboost.