Online drift compensation framework based on active learning for gas classification and concentration prediction.

Sensor drift is an urgent issue in the machine olfaction community. To date, most studies have focused on gas classification tasks based on an offline method, while neglecting concentration prediction and labeling cost. To permit multitasking including sensor drift, gas classification, concentration prediction, and labeling cost, this paper presents a novel online drift compensation framework based on active learning. Specifically, a Query Strategy for Gas Classification (QSGC) and a Query Strategy for Concentration Prediction (QSCP) are designed respectively, and an Online Domain-adaptive Extreme Learning Machine (ODELM) is proposed. First, the QSGC/QSCP is employed to select the most valuable samples for labeling in the gas classification task/concentration prediction task. Second, the ODELM utilizes only one labeled sample to update the prediction model, and thus adapts to evolving sensor drift. The proposed framework is compared with several state-of-the-art methods. Experimental results demonstrate that the proposed method achieves the best generalization ability with the minimum labeling cost. • An online drift compensation framework for gas sensors is proposed. • Two query strategies are designed to capture drift information. • An online domain-adaptive extreme learning machine is designed to continuously suppress the evolving drift by self-updating. • The framework can effectively handle gas classification and concentration prediction with drift at the lowest labeling cost. [ABSTRACT FROM AUTHOR]