Improving the Performance of Drifted/Shifted Electronic Nose Systems by Cross-Domain Transfer using Common Transfer Samples

Abstract Sensor drift/shift is a challenging and high-profile issue in the field of sensors and measurements. Because of the time variability and unpredictable properties of drift/shift, traditional compensation methods are costly and laborious. Considering the distribution alignments of different domains at both the feature and decision level, a unified double-level drift/shift compensation framework is presented to address sensor drift/shift. The framework consists of cross-domain feature subspace transfer learning coupled with a cross-domain adaptation extreme learning machine (CFST-AELM), which learns a low-dimensional discriminant subspace and a robust classifier using small numbers of labeled samples selected from the target domain. In general, the unified framework has the following advantages: (1) implementing anti-drift/shift at both the feature and decision level by cross-domain transfer learning which takes full advantage of transfer samples; (2) the subspace learning is a joint distribution adaptation algorithm and can be easily realized by eigenvalue decomposition; (3) more discriminative information of the source and target datasets remain after linear mapping; and (4) there is enhancement of the generalization performance of the model by adding the prediction error of transfer samples as a constraint of extreme learning machines (ELM) classification. The results suggest that compared with several state-of-the-art strategies, the CFST-AELM framework has superior performance in terms of classification accuracy.