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SDKT:Similar domain knowledge transfer for multivariate time series classification tasks
  • Jiaye Wen,
  • Wenan Zhou
Jiaye Wen
Beijing University of Posts and Telecommunications School of Computer Science
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Wenan Zhou
Beijing University of Posts and Telecommunications School of Computer Science

Corresponding Author:[email protected]

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Abstract

Multivariate time series data classification has a wide range of applications in reality. With rapid development of deep learning, convolutional networks are widely used in this task and have achieved the current best performance. However, due to high difficulty and cost of collecting this type of data, labeled data is still scarce. In some tasks, the model shows overfitting, resulting in relatively poor classification performance. In order to improve the classification effect under such situation, this paper proposes a novel classification method based on transfer learning - similar domain knowledge transfer (call SDKT for short). Firstly, we designed a multivariate time series domain distance calulation method (call MTSDDC for short), which helped selecting the source domain that are most similar to target domain; Secondly, we used ResNet as a pre-trained classifier, transfered the parameters of the similar domain network to the target domain network and continued to fine-tune the parameters. To verify our method, we conducted experiments on several public datasets. Our study has also shown that the transfer effect from the source domain to the target domain is highly negatively correlated with the distance between them, with an average pearson coefficient of -0.78. For the transfer of most similar source domain, compared to the ResNet model without transfer and the current best model, the average accuracy improvement on the datasets we used is 4.01% and 1.46% respectively.
05 Jul 2023Submitted to Computational Intelligence
05 Jul 2023Submission Checks Completed
05 Jul 2023Assigned to Editor
05 Jul 2023Review(s) Completed, Editorial Evaluation Pending
22 Jul 2024Reviewer(s) Assigned
31 Aug 2024Review(s) Completed, Editorial Evaluation Pending
01 Sep 2024Editorial Decision: Revise Major
26 Sep 20242nd Revision Received
28 Sep 2024Submission Checks Completed
28 Sep 2024Assigned to Editor
28 Sep 2024Reviewer(s) Assigned
28 Sep 2024Review(s) Completed, Editorial Evaluation Pending
28 Sep 2024Editorial Decision: Accept