Classification of multivariate weakly-labelled time-series with attention

TL;DR

This paper enhances multivariate weakly-labelled time-series classification by integrating attention mechanisms with CNN models, demonstrating improved accuracy on EEG datasets with noisy and redundant data.

Contribution

It introduces a novel CNN-LSTM architecture with attention algorithms tailored for weakly-labelled multivariate TSC, addressing a key gap in current models.

Findings

Attention improves classification accuracy on noisy EEG data.

CNN-LSTM with attention outperforms baseline models.

Effective context relevance extraction enhances weakly-labelled TSC.

Abstract

This research identifies a gap in weakly-labelled multivariate time-series classification (TSC), where state-of-the-art TSC models do not per-form well. Weakly labelled time-series are time-series containing noise and significant redundancies. In response to this gap, this paper proposes an approach of exploiting context relevance of subsequences from previous subsequences to improve classification accuracy. To achieve this, state-of-the-art Attention algorithms are experimented in combination with the top CNN models for TSC (FCN and ResNet), in an CNN-LSTM architecture. Attention is a popular strategy for context extraction with exceptional performance in modern sequence-to-sequence tasks. This paper shows how attention algorithms can be used for improved weakly labelledTSC by evaluating models on a multivariate EEG time-series dataset obtained using a commercial Emotiv headsets from participants performing various activities while driving. These time-series are segmented into sub-sequences and labelled to allow supervised TSC.