Online Drift Compensation by Adaptive Active Learning on Mixed Kernel for Electronic Noses

•Using active learning paradigm to deal with drift calibration.•We improved regular active learning with mixed kernel, adaptive selection and ranking normalization.•Different from laboratory testing, a more practical scenario is proposed and defined in this paper.•We performed a comprehensive evaluation of the proposed method on accuracy, parameter sensitivity, labeled instance distribution, computational complexity and labeling efficiency.