Multistrategy ensemble learning: reducing error by combining ensemble learning techniques

By Geoffrey I. Webb, Zijian Zheng. IEEE Transactions on Knowledge and Data Engineering, 2004

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Practical Ensemble Classification Error Bounds for Different Operating Points (2013, background): “In either case, the tradeoff between the probabilities of false alarm and missed detection is important to take into account.”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “A better generalization property of ensemble approach is often explained using the classic bias-variance decomposition analysis [14].”
Extremely randomized trees (2006, methodology): “On the other hand, since Boosting is a method known to reduce bias, it could possibly be combined with our Extra-Trees so as to reduce their bias (see Webb, 2000; Webb and Zheng, 2004, for a combination of Boosting and different randomization methods).”