Randomizing outputs to increase prediction accuracy
Randomizing outputs to increase prediction accuracy
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DOI：
10.1023/a:1007682208299
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发表时间：
2000-09-01
期刊：
影响因子：
7.5
通讯作者：
Breiman, L
中科院分区：
文献类型：
--
作者：
Breiman, L
Bagging和Boosting通过改变输入和输出来形成扰动训练集，在这些扰动训练集上生长预测器并将它们组合来减少错误。一个有趣的问题是，是否有可能通过单独扰动输出来获得类似的性能。实验了两种随机化输出的方法。一种称为输出拖尾，另一种称为输出翻转。两者都被证明是一贯做得比装袋。
Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, growing predictors on these perturbed training sets and combining them. An interesting question is whether it is possible to get comparable performance by perturbing the outputs alone. Two methods of randomizing outputs are experimented with. One is called output smearing and the other output flipping. Both are shown to consistently do better than bagging.