Gas-sensor drift is an important issue fading the gas identification performance of an Electronic Nose (E-nose). The most popular drift countermeasure is mathematical-model updating by periodic drift calibrations. Accordingly, drift-calibration-sample formation becomes a challenging issue in online detection because of rare opportunity for collecting drift-calibration samples. However, the class-imbalance problem may occur during such formation. Thus, we proposed an active drift-calibration-sample selection method including a new metric “classifier state” and an associated sample-evaluating procedure. To assess the proposed method, two benchmarks have been adopted. One is a public dataset while the other one is collected from our own E-nose system. Experimental results have demonstrated the superiority of the proposed method in several drift scenarios. Further, we visually explored the behind reason of the proposed method’s high performance. Additionally, a parameter sensitivity analysis was conducted. We conclude that the proposed methodology reduces the negative effect of class-imbalance problem successfully.

气体传感器漂移是淡化电子鼻（E型鼻）的气体识别性能的重要问题。最受欢迎的漂移对策是通过定期漂移校准来更新数学模型。因此，由于收集漂移校准样品的机会很少，因此漂移校准样品的形成在在线检测中成为一个具有挑战性的问题。但是，类不平衡问题可能会在这种形成过程中发生。因此，我们提出了一种主动的漂移校准样本选择方法，该方法包括新的度量“分类器状态”和相关的样本评估程序。为了评估提议的方法，采用了两个基准。一个是公共数据集，而另一个是从我们自己的电子鼻系统收集的。实验结果证明了该方法在几种漂移情况下的优越性。此外，我们在视觉上探索了该方法高性能的背后原因。另外，进行了参数敏感性分析。我们得出的结论是，所提出的方法成功地减少了类别不平衡问题的负面影响。