Abstract
A popular way of dealing with difficult problems is to organize a brainstorming session in which specialists from different fields share their knowledge, offering diverse points of view that complement each other to the point where they may inspire innovative solutions. Something similar can be done in machine learning, too. A group of classifiers is created in a way that makes each of them somewhat different. When they vote about the recommended class, their “collective wisdom” often compensates for each individual’s imperfections.
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Notes
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The name is an acronym of b ooststrap agg regation.
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References
Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Freund, Y. & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, Bari (pp. 148–156).
Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5, 197–227.
Wolpert, D. (1992). Stacked generalization. Neural Networks, 5, 241–259.
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Kubat, M. (2017). Induction of Voting Assemblies. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_9
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DOI: https://doi.org/10.1007/978-3-319-63913-0_9
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