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Volume 38 Issue 6
November  2023
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Zhang HB, Wang P, Zhang MM et al. Shapelet based two-step time series positive and unlabeled learning. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(6): 1387−1402 Nov. 2023. DOI: 10.1007/s11390-022-1320-9.
Citation: Zhang HB, Wang P, Zhang MM et al. Shapelet based two-step time series positive and unlabeled learning. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(6): 1387−1402 Nov. 2023. DOI: 10.1007/s11390-022-1320-9.
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Shapelet Based Two-Step Time Series Positive and Unlabeled Learning

  • School of Computer Science, Fudan University, Shanghai 200438, China

Funds: The work was supported by the National Key Research and Development Program of China under Grant No. 2020YFB1710001.
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  • Author Bio:

    Han-Bo Zhang received his B.S. degree in information engineering from East China University of Science and Technology, Shanghai, in 2013. He received his M.S. degree in computer system architecture from China Academic of Electronics and Information Technology, Beijing, in 2016. Now he is a Ph.D. candidate in School of Computer Science, Fudan University, Shanghai. His research interests include database, data mining, series data processing, and low quality data processing

    Peng Wang received his Ph.D. degree in computer science from Fudan University, Shanghai, in 2007. Now he is a professor in School of Computer Science, Fudan University, Shanghai. His research interests include database, data mining, and series data processing. He has published more than 30 papers in refereed international journals and conference proceedings

    Ming-Ming Zhang received her B.S. degree in software engineering from Henan University, Kaifeng, in 2019. She is currently studying for her M.S. degree in software engineering at Fudan University, Shanghai. Her research interests include motif discovery, time series similarity search, and data mining

    Wei Wang received his Ph.D. degree in computer science from Fudan University, Shanghai, in 1998. Now he is a professor in School of Computer Science of Fudan University, Shanghai. His research interests include database, data mining, and series data processing. He has published more than 100 papers in refereed international journals and conference proceedings

  • Corresponding author:

    pengwang5@fudan.edu.cn

  • Received Date: January 24, 2021
  • Accepted Date: December 21, 2022
    Abstract

  • In the last decade, there has been significant progress in time series classification. However, in real-world industrial settings, it is expensive and difficult to obtain high-quality labeled data. Therefore, the positive and unlabeled learning (PU-learning) problem has become more and more popular recently. The current PU-learning approaches of the time series data suffer from low accuracy due to the lack of negative labeled time series. In this paper, we propose a novel shapelet based two-step (2STEP) PU-learning approach. In the first step, we generate shapelet features based on the positive time series, which are used to select a set of negative examples. In the second step, based on both positive and negative time series, we select the final features and build the classification model. The experimental results show that our 2STEP approach can improve the average F1 score on 15 datasets by 9.1% compared with the baselines, and achieves the highest F1 score on 10 out of 15 time series datasets.

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