Constructing Ensembles for Better Ranking

Jin Huang, C. Ling
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Abstract

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.
构建集成以获得更好的排名
我们提出了一种新的算法,RankDE,使用一个额外的人工数据集来构建一个集成。RankDE旨在提高整体排名性能,这在许多机器学习应用中至关重要。该算法构建的人工数据集在排名方面与当前训练数据集不同。我们使用真实世界的数据集进行实验,将RankDE与Bagging、Adaboost、decor和Rankboost等传统和最先进的集成算法在排名方面进行比较。实验表明,当可用数据有限时,RankDE优于Bagging, decor, Adaboost和Rankboost。当有足够的训练数据可用时,它可以与decor和Adaboost竞争。
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