Class-imbalanced Time Series Adaptation via Multi-Expert Consistency Entropy Minimization

Wearable-based time series recognition aims to infer behavioral classes based on time series signals collected by sensors. Domain Adaptation (DA) methods have significantly improved the performance on out-of-domain test classification tasks. However, highly imbalanced label distribution in real-world tasks hinders the practicality of DA methods. In this paper, we propose a Multi-Expert Consistency Entropy Minimization (MECEM) method for class-imbalanced domain adaptation. Through the incorporation of a multi-expert committee mechanism, we recalibrate decision boundaries in response to imbalanced source distributions, thereby mitigating bias towards minority classes and promoting fairness in committee decisions. Furthermore, we introduce a time-frequency dynamic augmentation mechanism, which simultaneously addresses domain shift on the target domain and dynamically adapts the augmentation library for the classification task. The MECEM method facilitates the propagation of consistency entropy during backpropagation, conveying both the reliability of pseudo-labels and the classification task relevance of augmentation techniques. Experiments across various tasks on activity recognition and Parkinson’s tremor grading demonstrate the superiority of our method.