CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive
  Policies For Time Series
Tien-Yu Chang Hao Dai Vincent S. Tseng
Data Augmentation is a common technique used to enhance the performance of
deep learning models by expanding the training dataset. Automatic Data
Augmentation (ADA) methods are getting popular because of their capacity to
generate policies for various datasets. However, existing ADA methods primarily
focused on overall performance improvement, neglecting the problem of
class-dependent bias that leads to performance reduction in specific classes.
This bias poses significant challenges when deploying models in real-world
applications. Furthermore, ADA for time series remains an underexplored domain,
highlighting the need for advancements in this field. In particular, applying
ADA techniques to vital signals like an electrocardiogram (ECG) is a compelling
example due to its potential in medical domains such as heart disease
diagnostics.
  We propose a novel deep learning-based approach called Class-dependent
Automatic Adaptive Policies (CAAP) framework to overcome the notable
class-dependent bias problem while maintaining the overall improvement in
time-series data augmentation. Specifically, we utilize the policy network to
generate effective sample-wise policies with balanced difficulty through class
and feature information extraction. Second, we design the augmentation
probability regulation method to minimize class-dependent bias. Third, we
introduce the information region concepts into the ADA framework to preserve
essential regions in the sample. Through a series of experiments on real-world
ECG datasets, we demonstrate that CAAP outperforms representative methods in
achieving lower class-dependent bias combined with superior overall
performance. These results highlight the reliability of CAAP as a promising ADA
method for time series modeling that fits for the demands of real-world
applications.