This paper presents a generalization of the bagging procedure by using smoothed bootstrap with bagging, a procedure we call generalized bagging. Our generalized bagging method unifies input and output smearing, in the sense that noise is added to both the input and the output, so that input smearing and output smearing become special cases of generalized bagging. We discuss the choice of optimal smoothing parameter to control the variance of the added noise in the smoothed bootstrap. Our simulation studies show that the proposed method outperforms other competing methods, when the variance of the error term is large. We also demonstrate the performance of the proposed procedure with real datasets.

本文通过使用带套袋的平滑引导程序介绍了套袋过程的一般化，这一过程称为广义套袋。我们的广义装袋方法统一了输入和输出拖尾，即在输入和输出上都添加了噪声，因此输入拖尾和输出拖尾成为广义装袋的特殊情况。我们讨论了最佳平滑参数的选择，以控制平滑引导程序中添加噪声的方差。我们的仿真研究表明，当误差项的方差较大时，该方法优于其他竞争方法。我们还演示了使用实际数据集提出的程序的性能。