Smooth unsupervised domain adaptation considering uncertainties

Collecting sufficient labeled data is time-consuming and even infeasible in streaming data applications. Unsupervised domain adaptation is a reasonable solution that has recently gained attention. However, most efforts have focused on stationary environments and have ignored the uncertainties that arise from mismatched distributions. Therefore, the proposed method addresses the classification of samples in streaming data in the presence of concept drift, employing a heterogeneous unsupervised domain adaptation method. Accordingly, a fuzzy rough set-based sample weighting approach is introduced to modulate the impact of uncertainties on feature alignment in non-stationary environments. The domain adaptation is carried out through a fuzzy approach and is optimized by a heuristic optimization technique that reduces the sensitivity to tunable parameters. Moreover, an incremental classifier is designed to enable rapid adaptation to changes. The advantages of the proposed method encompass an effective avoidance of excessive alignment, training cost optimization, and the gradual reduction in dependency on the source domain for domain adaptation. Regarding different types of concept drift, experiments on several tasks taken from four benchmark datasets demonstrate the superiority of the proposed method in terms of accuracy and computational time.