A Robust Bagging Method Using Median as a Combination Rule
A Robust Bagging Method Using Median as a Combination Rule
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一种使用中位数作为组合规则的鲁棒装袋方法
DOI：
10.1109/cit.2008.workshops.56
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Hideo Hirose
中科院分区：
文献类型：
--
作者：
F. Zaman;Hideo Hirose
已知Bagging在提高不稳定分类器的预测精度方面是成功的。在装袋预测器中，使用来自训练集的自举样本构建预测器，然后聚合以形成装袋预测器。鲁棒装袋丢弃产生极端错误率的自举分类器，如由袋外错误率估计的，并使用鲁棒位置估计器“中值”对剩余的分类器进行联合收割机组合。在本文中，我们试图探索鲁棒套袋的优点。我们进行了几个基准数据集上的实验，并建议从结果中，鲁棒装袋执行非常相似的标准装袋时，适用于不稳定的基础分类，如决策树，但更好地执行时，适用于更稳定的基础分类，如Fisher线性判别分析和最近均值分类。
Bagging has been known to be successful in increasing the accuracy of prediction of the unstable classifiers. In bagging predictors are constructed using bootstrap samples from the training sets and then aggregated to form a bagged predictor. The robust bagging discard the bootstrapped classifiers generating extreme error rates, as estimated by the out-of-bag error rate and to combine over the remaining ones using the robust location estimator,'median'. In this paper we try to explore the advantages of robust bagging. We carried out experiments on several benchmark data sets and suggest from the results that robust bagging performs quite similar compare to the standard bagging when applied to unstable base classifiers such as decision trees, but performs better when applied to more stable base classifiers as Fisher linear discriminant analysis and nearest mean classifier.