In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression, such as linear regression or Gaussian process (GP). Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for GP. In this article, we propose two new active learning algorithms for the GP with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact of uncertainties and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage.

中文翻译：

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考虑不确定性的高斯过程主动学习在复合材料机身形状控制中的应用


在机器学习领域，主动学习是一种迭代数据选择算法，用于在有限的训练样本下最大化信息获取并提高模型性能。它非常有用，特别是对于训练样本昂贵、耗时或难以获得的工业应用。现有的方法主要侧重于分类的主动学习，还有一些方法是针对回归设计的，例如线性回归或高斯过程（GP）。实验数据中不可避免地存在测量误差和固有输入噪声的不确定性，这进一步影响了建模性能。现有的主动学习方法没有考虑到 GP 的这些不确定性。在本文中，我们针对不确定性的GP提出了两种新的主动学习算法，即基于方差的加权主动学习算法和D最优加权主动学习算法。通过数值研究，我们表明所提出的方法可以考虑不确定性的影响并实现更好的预测性能。该方法已应用于改进复合材料机身自动形状控制的预测模型。