论文标题
域自适应引导汇总
Domain Adaptive Bootstrap Aggregating
论文作者
论文摘要
当用于训练预测算法的数据和当前数据之间存在分布变化时，性能可能会受到影响。这被称为域适应问题。 Bootstrap聚合或包装是提高预测算法稳定性的一种流行方法，同时降低了差异并防止过度拟合。本文提出了一种域自适应装袋方法，再加上新的迭代最近的邻居采样器。关键思想是从培训数据中绘制引导样本，以使其分布等于新的测试数据的分布。所提出的方法提供了可以应用于任意分类器的一般整体框架。我们进一步修改了该方法，以允许在训练数据中与异常值相对应的测试数据中的异常样本。提供了理论支持，并将方法与模拟和实际数据应用中的替代方案进行了比较。
When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer. This is known as the domain adaptation problem. Bootstrap aggregating, or bagging, is a popular method for improving stability of predictive algorithms, while reducing variance and protecting against over-fitting. This article proposes a domain adaptive bagging method coupled with a new iterative nearest neighbor sampler. The key idea is to draw bootstrap samples from the training data in such a manner that their distribution equals that of new testing data. The proposed approach provides a general ensemble framework that can be applied to arbitrary classifiers. We further modify the method to allow anomalous samples in the test data corresponding to outliers in the training data. Theoretical support is provided, and the approach is compared to alternatives in simulations and real data applications.
