Approximate linear dependence criteria with active learning for smart soft sensor design

Abstract

As a semi-supervised machine learning strategy, active learning has recently been introduced into the soft sensing area for the performance enhancement and the save of human efforts. Active learning is capable to automatically select the most informative unlabeled samples for labeling, thus the costs related to human annotation can be reduced. Instead of randomly labeling data samples, in this paper, we employ kernel approximate linear dependence (ALD) to evaluate each unlabeled data samples, and the data samples with large evaluation values are labeled for model updating. Comparative study results show that the ALD based active learning strategy driven soft sensor obtains better prediction performance than the random selection strategy driven soft sensor.