标题： 堆叠与稳定性 显示英文标题

标题： Stacking and stability

摘要： 堆叠是一种结合多个模型以提高预测准确性的通用方法。由于其元学习的本质，它已在不同领域找到了各种应用。然而，我们对堆叠为何以及如何有效工作的理解仍然停留在直观层面，并缺乏理论上的洞察力。本文使用学习算法的稳定性作为基本分析框架来解决这一问题。为此，我们分析了堆叠、包堆叠和DAG堆叠的假设稳定性，并建立了包堆叠与加权装袋之间的联系。我们证明了堆叠的假设稳定性是基础模型和组合器假设稳定性的乘积。此外，在包堆叠和DAG堆叠中，假设稳定性取决于生成训练集副本所使用的抽样策略。我们的研究结果表明：1）子抽样和自助抽样可以提高堆叠的稳定性；2）堆叠可以提高子袋装法和袋装法的稳定性。 显示英文摘要

摘要： Stacking is a general approach for combining multiple models toward greater predictive accuracy. It has found various application across different domains, ensuing from its meta-learning nature. Our understanding, nevertheless, on how and why stacking works remains intuitive and lacking in theoretical insight. In this paper, we use the stability of learning algorithms as an elemental analysis framework suitable for addressing the issue. To this end, we analyze the hypothesis stability of stacking, bag-stacking, and dag-stacking and establish a connection between bag-stacking and weighted bagging. We show that the hypothesis stability of stacking is a product of the hypothesis stability of each of the base models and the combiner. Moreover, in bag-stacking and dag-stacking, the hypothesis stability depends on the sampling strategy used to generate the training set replicates. Our findings suggest that 1) subsampling and bootstrap sampling improve the stability of stacking, and 2) stacking improves the stability of both subbagging and bagging.