Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
This work addresses the problem of efficiently selecting appropriate source datasets for transfer learning in time series classification, which is beneficial for researchers and practitioners aiming to improve model performance with less data.
This paper proposes a new method for selecting and combining multiple source datasets for transfer learning in time series classification. Their method uses shapelet discovery to measure dataset transferability, enabling a single, efficient computation for source selection across various architectures, and demonstrates improved performance of temporal CNNs on time series datasets.
Transfer learning is a common practice that alleviates the need for extensive data to train neural networks. It is performed by pre-training a model using a source dataset and fine-tuning it for a target task. However, not every source dataset is appropriate for each target dataset, especially for time series. In this paper, we propose a novel method of selecting and using multiple datasets for transfer learning for time series classification. Specifically, our method combines multiple datasets as one source dataset for pre-training neural networks. Furthermore, for selecting multiple sources, our method measures the transferability of datasets based on shapelet discovery for effective source selection. While traditional transferability measures require considerable time for pre-training all the possible sources for source selection of each possible architecture, our method can be repeatedly used for every possible architecture with a single simple computation. Using the proposed method, we demonstrate that it is possible to increase the performance of temporal convolutional neural networks (CNN) on time series datasets.