Automatic Data Augmentation with Adaptive Policies to Mitigate Class-Dependent Bias in Time Series Classification

The core message of this work is to propose a novel Class-dependent Automatic Adaptive Policies (CAAP) framework that aims to tackle the class-dependent bias problem in automatic data augmentation while improving overall performance for supervised learning-based time series classification.