Model Selection with a Shapelet-Based Distance Measure for Multi-source Transfer Learning in Time Series Classification

Jiseok Lee, Brian Kenji Iwana

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

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.

本文言語英語
ホスト出版物のタイトルPattern Recognition - 27th International Conference, ICPR 2024, Proceedings
編集者Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal
出版社Springer Science and Business Media Deutschland GmbH
ページ160-175
ページ数16
ISBN(印刷版)9783031783975
DOI
出版ステータス出版済み - 2025
イベント27th International Conference on Pattern Recognition, ICPR 2024 - Kolkata, インド
継続期間: 12月 1 202412月 5 2024

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
15327 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議27th International Conference on Pattern Recognition, ICPR 2024
国/地域インド
CityKolkata
Period12/1/2412/5/24

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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