An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging
Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positiv...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7414 |
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| author | Jing Li Haowen Zhang Yabo Dong Tongbin Zuo Duanqing Xu |
| author_facet | Jing Li Haowen Zhang Yabo Dong Tongbin Zuo Duanqing Xu |
| author_sort | Jing Li |
| collection | DOAJ |
| description | Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive unlabeled time series classification problem (<i>PUTSC</i>), which refers to automatically labelling the large unlabeled set <i>U</i> based on a small positive labeled set <i>PL</i>. The self-training (<i>ST</i>) is the most widely used method for solving the <i>PUTSC</i> problem and has attracted increased attention due to its simplicity and effectiveness. The existing <i>ST</i> methods simply employ the <i>one-nearest-neighbor</i> (<i>1NN)</i> formula to determine which unlabeled time-series should be labeled. Nevertheless, we note that the <i>1NN</i> formula might not be optimal for <i>PUTSC</i> tasks because it may be sensitive to the initial labeled data located near the boundary between the positive and negative classes. To overcome this issue, in this paper we propose an exploratory methodology called <i>ST-average</i>. Unlike conventional <i>ST</i>-based approaches, <i>ST-average</i> utilizes the average sequence calculated by DTW barycenter averaging technique to label the data. Compared with any individuals in <i>PL</i> set, the average sequence is more representative. Our proposal is insensitive to the initial labeled data and is more reliable than existing <i>ST</i>-based methods. Besides, we demonstrate that <i>ST-average</i> can naturally be implemented along with many existing techniques used in original <i>ST</i>. Experimental results on public datasets show that <i>ST-average</i> performs better than related popular methods. |
| first_indexed | 2024-03-10T05:52:06Z |
| format | Article |
| id | doaj.art-8e7f01c330cb45f9aa4fc325ef7b54b0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:06Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8e7f01c330cb45f9aa4fc325ef7b54b02023-11-22T21:41:16ZengMDPI AGSensors1424-82202021-11-012121741410.3390/s21217414An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter AveragingJing Li0Haowen Zhang1Yabo Dong2Tongbin Zuo3Duanqing Xu4College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaTraditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive unlabeled time series classification problem (<i>PUTSC</i>), which refers to automatically labelling the large unlabeled set <i>U</i> based on a small positive labeled set <i>PL</i>. The self-training (<i>ST</i>) is the most widely used method for solving the <i>PUTSC</i> problem and has attracted increased attention due to its simplicity and effectiveness. The existing <i>ST</i> methods simply employ the <i>one-nearest-neighbor</i> (<i>1NN)</i> formula to determine which unlabeled time-series should be labeled. Nevertheless, we note that the <i>1NN</i> formula might not be optimal for <i>PUTSC</i> tasks because it may be sensitive to the initial labeled data located near the boundary between the positive and negative classes. To overcome this issue, in this paper we propose an exploratory methodology called <i>ST-average</i>. Unlike conventional <i>ST</i>-based approaches, <i>ST-average</i> utilizes the average sequence calculated by DTW barycenter averaging technique to label the data. Compared with any individuals in <i>PL</i> set, the average sequence is more representative. Our proposal is insensitive to the initial labeled data and is more reliable than existing <i>ST</i>-based methods. Besides, we demonstrate that <i>ST-average</i> can naturally be implemented along with many existing techniques used in original <i>ST</i>. Experimental results on public datasets show that <i>ST-average</i> performs better than related popular methods.https://www.mdpi.com/1424-8220/21/21/7414positive unlabeled time series classificationself-trainingdynamic time warpingDTW barycenter averaging |
| spellingShingle | Jing Li Haowen Zhang Yabo Dong Tongbin Zuo Duanqing Xu An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging Sensors positive unlabeled time series classification self-training dynamic time warping DTW barycenter averaging |
| title | An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging |
| title_full | An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging |
| title_fullStr | An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging |
| title_full_unstemmed | An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging |
| title_short | An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging |
| title_sort | improved self training method for positive unlabeled time series classification using dtw barycenter averaging |
| topic | positive unlabeled time series classification self-training dynamic time warping DTW barycenter averaging |
| url | https://www.mdpi.com/1424-8220/21/21/7414 |
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