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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Autores principales:	Jing Li, Haowen Zhang, Yabo Dong, Tongbin Zuo, Duanqing Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	positive unlabeled time series classification self-training dynamic time warping DTW barycenter averaging Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8e7f01c330cb45f9aa4fc325ef7b54b0
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spelling	oai:doaj.org-article:8e7f01c330cb45f9aa4fc325ef7b54b02021-11-11T19:20:00ZAn Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging10.3390/s212174141424-8220https://doaj.org/article/8e7f01c330cb45f9aa4fc325ef7b54b02021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7414https://doaj.org/toc/1424-8220Traditional 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.Jing LiHaowen ZhangYabo DongTongbin ZuoDuanqing XuMDPI AGarticlepositive unlabeled time series classificationself-trainingdynamic time warpingDTW barycenter averagingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7414, p 7414 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	positive unlabeled time series classification self-training dynamic time warping DTW barycenter averaging Chemical technology TP1-1185
spellingShingle	positive unlabeled time series classification self-training dynamic time warping DTW barycenter averaging Chemical technology TP1-1185 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
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.
format	article
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
title	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_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_sort	improved self-training method for positive unlabeled time series classification using dtw barycenter averaging
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8e7f01c330cb45f9aa4fc325ef7b54b0
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_version_	1718431538761170944

An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging

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