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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Reranking for Stacking Ensemble Learning
verfasst von : Buzhou Tang, Qingcai Chen, Xuan Wang, Xiaolong Wang
Erschienen in: Neural Information Processing. Theory and Algorithms
Verlag: Springer Berlin Heidelberg
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Ensemble learning refers to the methods that combine multiple models to improve the performance. Ensemble methods, such as stacking, have been intensively studied, and can bring slight performance improvement. However, there is no guarantee that a stacking algorithm outperforms all base classifiers. In this paper, we propose a new stacking algorithm, where the predictive scores of each possible class label returned by the base classifiers are firstly collected by the meta-learner, and then all possible class labels are reranked according to the scores. This algorithm is able to find the best linear combination of the base classifiers on the training samples, which make sure it outperforms all base classifiers during training process. The experiments conducted on several public datasets show that the proposed algorithm outperforms the baseline algorithms and several state-of-the-art stacking algorithms.