Bagging predictors
Bagging predictors
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DOI：
10.1007/bf00058655
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发表时间：
1996-08-01
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 predictors是一种用于生成多个版本的预测器并使用这些预测器来凝胶聚合预测器的方法。当预测数值结果时，聚合对版本进行平均，并且当预测类时进行多个投票。多个版本通过对学习集进行自举复制并将其用作新的学习集来形成。在真实的和模拟数据集上使用分类和回归树以及线性回归中的子集选择进行测试，结果表明，装袋可以在准确性上获得实质性的提高。关键因素是预测方法的不稳定性。如果扰动学习集可以导致构建的预测器发生重大变化，那么bagging可以提高准确性。
Bagging predictors is a method for generating multiple versions of a predictor and using these to gel an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.