期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：2
页码：1-7
DOI：10.1088/1755-1315/252/2/022095
出版社：IOP Publishing
摘要：In biological measuring instruments design, it is time-consuming and expensive to acquire sufficient samples for calibration process. Transfer learning has provided an efficient approach for this problem by leveraging the labeled samples from calibration channels, considered as source domain, to annotate the target domain which has few labels under actual working conditions. In this paper, we design a general transfer regression framework improved by data-augmented ensemble learning called DA-TRTs. First, we modify two-stage TrAdaBoost.R2 to TrAdaBoost. RT (TRT) which concentrates more on hard examples during transfer boosting. Second, model stacking framework is introduced to improve the performance of regression, calling TRT as a base learner. Then, we introduce a simple data augmentation routine, based on label smoothing assumption. Data augmentation improves the strength in diversity of base learners and implicitly controls model complexity. Finally, to illustrate the performance of DA-TRTs, we conduct experiments on synthetic datasets.
