Adaptive model training strategy for continuous classification of time series

Chenxi Sun; Hongyan Li; Moxian Song; Derun Cai; Baofeng Zhang; Shenda Hong

doi:10.1007/s10489-022-04433-z

Adaptive model training strategy for continuous classification of time series

Appl Intell (Dordr). 2023 Feb 11:1-19. doi: 10.1007/s10489-022-04433-z. Online ahead of print.

Authors

Chenxi Sun^{1

2}, Hongyan Li^{1

2}, Moxian Song^{1

2}, Derun Cai^{1

2}, Baofeng Zhang^{1

2}, Shenda Hong^{3

4}

Affiliations

¹ School of Intelligence Science and Technology, Peking University, Beijing, China.
² Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China.
³ National Institute of Health Data Science, Peking University, Beijing, China.
⁴ Institute of Medical Technology, Health Science Center of Peking University, Beijing, China.

Abstract

The classification of time series is essential in many real-world applications like healthcare. The class of a time series is usually labeled at the final time, but more and more time-sensitive applications require classifying time series continuously. For example, the outcome of a critical patient is only determined at the end, but he should be diagnosed at all times for timely treatment. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS). Different from the existing single-shot classification, the key of CCTS is to model multiple distributions simultaneously due to the dynamic evolution of time series. But the deep learning model will encounter intertwined problems of catastrophic forgetting and over-fitting when learning multi-distribution. In this work, we found that the well-designed distribution division and replay strategies in the model training process can help to solve the problems. We propose a novel Adaptive model training strategy for CCTS (ACCTS). Its adaptability represents two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays important samples adaptive to their contribution to the model. Experiments on four real-world datasets show that our method outperforms all baselines.

Keywords: Continuous classification of time series; Medical applications; Model training strategy.

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