Batch-Mode Active Learning of Gaussian Process Regression With Maximum Model Change

This article proposes a batch-mode active learning (AL) method of Gaussian process regression (GPR), which is based on the expected model change maximization. Unlike existing strategies for measuring the model change, weight’s information gain (WIG) caused by model training is introduced to measure the model change in this article, which has a lower computational cost. First, two methods for calculating the WIG of the GPR are given from the perspective of accuracy and easy realization, respectively. Then, two AL algorithms are designed by using enumeration and greedy sampling. Finally, experiments on three datasets from various domains have verified the effectiveness of the proposed AL algorithms.