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

Core Concepts

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.

Abstract

This work proposes the CAAP framework to address the class-dependent bias problem in automatic data augmentation (ADA) for time series classification tasks. The key components of the CAAP framework are:

Class Adaption Policy Network: This module learns augmentation policies that capture the relationship between sample information (feature and label) and suitable augmentation policies. It introduces a policy network to efficiently search for class-wise augmentation policies.

Class-dependent Regulation: This module adjusts the weight of the no-augmentation transformation based on the performance of each class during the search phase, effectively addressing class-dependent bias while training the task model.

Information Region Adaption: This module preserves essential information in the data sample throughout the augmentation process, addressing the challenge of maintaining informative regions during augmentation.

The experiments on real-world ECG datasets demonstrate that the CAAP framework outperforms representative ADA methods in achieving lower class-dependent bias combined with superior overall performance. The results highlight the reliability of CAAP as a promising ADA method for time series modeling that fits the demands of real-world applications.

Stats

The authors report the following key metrics and figures:
"Accuracy (ACC) and macro recall (Recall) of each method."
"Sample-wise class bias (Swise bias), improvement (Swise improve) and gain (sample-wise gain) of each method."

Quotes

"Our CAAP method demonstrates superior accuracy and macro recall across various datasets, exhibiting consistent performance improvement regardless of class numbers."
"The experimental results indicate that our class-wise augmentation policy searching process can learn less biased policies compared to competitive methods."
"Our CAAP method achieves superior overall performance and class-dependent bias for most datasets, especially those with waveform properties or larger class numbers."

Key Insights Distilled From

CAAP

by Tien-Yu Chan... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00898.pdf

Deeper Inquiries

How can the CAAP framework be extended to other time series domains beyond ECG, such as speech recognition or financial time series

The CAAP framework can be extended to other time series domains beyond ECG by adapting the augmentation policies and modules to suit the specific characteristics of the new datasets. For speech recognition, the Class Adaption Policy Network can be modified to consider features unique to speech signals, such as phonemes or intonation patterns. Additionally, the Information Region Adaption module can be adjusted to preserve important speech features during augmentation, such as specific frequency ranges or phonetic transitions. In financial time series, the framework can be tailored to capture key indicators or patterns relevant to financial data, ensuring that the augmentation policies enhance the model's performance in predicting market trends or investment decisions.

What are the potential limitations of the CAAP framework, and how could it be further improved to handle more complex or diverse time series datasets

One potential limitation of the CAAP framework is the reliance on predefined transformations in the augmentation policy. To handle more complex or diverse time series datasets, the framework could be improved by incorporating a mechanism for adaptive transformation selection based on the dataset's characteristics. This adaptive approach could involve dynamically adjusting the augmentation policies during training based on the model's performance and the dataset's specific requirements. Additionally, integrating more advanced techniques, such as reinforcement learning or meta-learning, could enhance the framework's ability to adapt to diverse datasets and optimize the balance between accuracy and class-dependent bias.

Given the trade-off between accuracy and class-dependent bias, how could the CAAP framework be adapted to allow users to adjust the balance between these two objectives based on their specific needs and priorities

To allow users to adjust the balance between accuracy and class-dependent bias based on their specific needs and priorities, the CAAP framework could incorporate customizable parameters for controlling the trade-off. Users could define weightings or thresholds that prioritize either accuracy improvement or bias reduction during the augmentation policy search. Additionally, the framework could include interactive visualization tools that display the trade-off relationship in real-time, enabling users to make informed decisions about the augmentation policies. By providing flexibility in adjusting the balance between accuracy and bias, the CAAP framework can cater to a wide range of applications and user preferences.