Sun Fangyu, Sun Ruihong, Yan Jia
WESTA College, Southwest University, Chongqing 400715, China.
College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Micromachines (Basel). 2022 Aug 5;13(8):1260. doi: 10.3390/mi13081260.
The problem of drift in the electronic nose (E-nose) is an important factor in the distortion of data. The existing active learning methods do not take into account the misalignment of the data feature distribution between different domains due to drift when selecting samples. For this, we proposed a cross-domain active learning (CDAL) method based on the Hellinger distance (HD) and maximum mean difference (MMD). In this framework, we weighted the HD with the MMD as a criterion for sample selection, which can reflect as much drift information as possible with as few labeled samples as possible. Overall, the CDAL framework has the following advantages: (1) CDAL combines active learning and domain adaptation to better assess the interdomain distribution differences and the amount of information contained in the selected samples. (2) The introduction of a Gaussian kernel function mapping aligns the data distribution between domains as closely as possible. (3) The combination of active learning and domain adaptation can significantly suppress the effects of time drift caused by sensor ageing, thus improving the detection accuracy of the electronic nose system for data collected at different times. The results showed that the proposed CDAL method has a better drift compensation effect compared with several recent methodological frameworks.
电子鼻（E-nose）中的漂移问题是数据失真的一个重要因素。现有的主动学习方法在选择样本时没有考虑到由于漂移导致的不同域之间数据特征分布的不一致。为此，我们提出了一种基于赫林格距离（HD）和最大均值差异（MMD）的跨域主动学习（CDAL）方法。在此框架中，我们将HD与MMD加权作为样本选择标准，这样可以用尽可能少的标记样本反映尽可能多的漂移信息。总体而言，CDAL框架具有以下优点：（1）CDAL将主动学习和域适应相结合，以更好地评估域间分布差异以及所选样本中包含的信息量。（2）引入高斯核函数映射可使域间数据分布尽可能紧密对齐。（3）主动学习和域适应相结合可以显著抑制传感器老化引起的时间漂移的影响，从而提高电子鼻系统对不同时间采集的数据的检测精度。结果表明，与最近的几个方法框架相比，所提出的CDAL方法具有更好的漂移补偿效果。
