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Abstract:
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Bootstrap aggregation, or bagging, is a method of reducing the prediction error of a statistical learner. In the context of the prediction problem, the goal of bagging is to construct a new learner which is the expectation of the original over the empirical or sample distribution function. In nearly all cases, the expectation cannot be computed analytically, and bootstrap sampling is used to produce an approximation. The k-nearest neighbor learners are exceptions to this generalization, and exact bagging is very easy. In addition to computational savings there are interesting opportunities to study the bagging properties of k-nearest neighbor learners and to develop new exact and nearly exact bootstrap k-nearest neighbor learners.
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