Self-labeling techniques for semi-supervised time series classification: an empirical study
An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and th...
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| Format: | Article |
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Springer
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44845/ |
| _version_ | 1848797011483033600 |
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| author | González, Mabel Bergmeir, Christoph Triguero, Isaac Rodríguez, Yanet Benítez, José M. |
| author_facet | González, Mabel Bergmeir, Christoph Triguero, Isaac Rodríguez, Yanet Benítez, José M. |
| author_sort | González, Mabel |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context. |
| first_indexed | 2025-11-14T19:57:06Z |
| format | Article |
| id | nottingham-44845 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:57:06Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-448452020-05-04T18:59:27Z https://eprints.nottingham.ac.uk/44845/ Self-labeling techniques for semi-supervised time series classification: an empirical study González, Mabel Bergmeir, Christoph Triguero, Isaac Rodríguez, Yanet Benítez, José M. An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context. Springer 2017-08-08 Article PeerReviewed González, Mabel, Bergmeir, Christoph, Triguero, Isaac, Rodríguez, Yanet and Benítez, José M. (2017) Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems . ISSN 0219-3116 Semi-supervised classification; Self-labeled; Time series classification; Semi-supervised learning; Self-training https://link.springer.com/article/10.1007%2Fs10115-017-1090-9 doi:10.1007/s10115-017-1090-9 doi:10.1007/s10115-017-1090-9 |
| spellingShingle | Semi-supervised classification; Self-labeled; Time series classification; Semi-supervised learning; Self-training González, Mabel Bergmeir, Christoph Triguero, Isaac Rodríguez, Yanet Benítez, José M. Self-labeling techniques for semi-supervised time series classification: an empirical study |
| title | Self-labeling techniques for semi-supervised time series classification: an empirical study |
| title_full | Self-labeling techniques for semi-supervised time series classification: an empirical study |
| title_fullStr | Self-labeling techniques for semi-supervised time series classification: an empirical study |
| title_full_unstemmed | Self-labeling techniques for semi-supervised time series classification: an empirical study |
| title_short | Self-labeling techniques for semi-supervised time series classification: an empirical study |
| title_sort | self-labeling techniques for semi-supervised time series classification: an empirical study |
| topic | Semi-supervised classification; Self-labeled; Time series classification; Semi-supervised learning; Self-training |
| url | https://eprints.nottingham.ac.uk/44845/ https://eprints.nottingham.ac.uk/44845/ https://eprints.nottingham.ac.uk/44845/ |