Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A randomized model ensemble approach for reconstructing signals from faulty sensors
Expert Systems with Applications: An International Journal
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Bagging ensemble selection for regression
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formally analyze the optimal linear combination of two base ensembles, which is then extended to multiple individual ensembles via pairwise combinations. The Cocktail ensemble approach is proposed based on this analysis. Experiments over a broad range of data sets show that the proposed approach outperforms the individual ensembles, two other methods of ensemble combination, and two stateof-the-art regression approaches.