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.