Definition
Boosting is a kind of ensemble method which produces a strong learner that is capable of making very accurate predictions by combining rough and moderately inaccurate learners (which are called as base learners or weak learners). In particular, Boosting sequentially trains a series of base learners by using a base learning algorithm, where the training examples wrongly predicted by a base learner will receive more attention from the successive base learner. After that, it generates a final strong learner through a weighted combination of these base learners.
Historical Background
In 1988, Kearns and Valiant posed an interesting question for the research of computational learning theory, i.e., whether a weak learning algorithm that performs just slightly better than random guess can be “boosted” into an arbitrarily accurate strong learning algorithm. In other words, whether two complexity classes, weakly learnable and strongly learnableproblems, are equal. In 1989, Schapire...
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Recommended Reading
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Zhou, ZH. (2009). Boosting. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_568
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