Definition
Every ensemble learning strategy might be expected to have unique effects on the base learner. Combining multiple ensemble learning algorithms might hence be expected to provide benefit. For example, Multi-Boosting combines AdaBoost and a variant of Bagging, obtaining most of AdaBoost’s bias reduction coupled with most of Bagging’s variance reduction. Similarly, Random Forests combines Bagging’s variance reduction with Random Subspaces’ bias reduction.
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Webb GI, Zheng Z (2004) Multistrategy ensemble learning: reducing error by combining ensemble learning techniques. IEEE Trans Knowl Data Eng 16(8): 980–991
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(2017). Multistrategy Ensemble Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_574
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_574
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