Uncertainty-aware hierarchical segment-channel attention mechanism for reliable and interpretable multichannel signal classification

Jiyoon Lee, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Multichannel signal data analysis has been crucial in various industrial applications, such as human activity recognition, vehicle failure predictions, and manufacturing equipment monitoring. Recently, deep neural networks have come into use for multichannel signal data because of their ability to automatically extract useful features from complex multichannel signals. However, deep neural networks are black-box models whose internal working mechanisms cannot be put in a form readily understood by humans. To address this issue, we have proposed an uncertainty-aware hierarchical segment-channel attention model that consists of a time segment and channel level attentions. The hierarchical attention mechanism enables a neural network to identify important time segments and channels critical for prediction, making the model explainable. In addition, the model uses variational inferences to provide uncertainty information that yields a confidence interval that can be easily explained. We conducted experiments on simulated and real-world datasets to demonstrate the usefulness and applicability of our method. The results confirm that our method can attend to important time segments and sensors while achieving better classification performance.

Original languageEnglish
Pages (from-to)68-86
Number of pages19
JournalNeural Networks
Volume150
DOIs
Publication statusPublished - 2022 Jun

Keywords

  • Attention mechanism
  • Bayesian neural network
  • Explainable neural network
  • Multichannel signal
  • Multivariate time series

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Uncertainty-aware hierarchical segment-channel attention mechanism for reliable and interpretable multichannel signal classification'. Together they form a unique fingerprint.

Cite this