Robust expected model change for active learning in regression

Active learning methods have been introduced to reduce the expense of acquiring labeled data. To solve regression problems with active learning, several expected model change maximization strategies have been developed to select the samples that are likely to greatly affect the current model. However, some of the selected samples may be outliers, which can result in poor estimation performance. To address this limitation, this study proposes an active learning framework that adopts an expected model change that is robust for both linear and nonlinear regression problems. By embedding local outlier probability, the learning framework aims to avoid outliers when selecting the samples that result in the greatest change to the current model. Experiments are conducted on synthetic and benchmark data to compare the performance of the proposed method with that of existing methods. The experimental results demonstrate that the proposed active learning algorithm outperforms its counterparts.