Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage
This work addresses a domain-specific problem in industrial manufacturing where data acquisition is costly, offering incremental improvements by extending active learning to handle uncertainties in Gaussian processes.
The authors tackled the problem of active learning for Gaussian process regression under data uncertainties, proposing two new algorithms that incorporate measurement and input noise to improve prediction performance, with application to composite fuselage shape control.
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 the 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. 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 Gaussian process. In this paper, we propose two new active learning algorithms for the Gaussian process 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 from uncertainties, and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage.