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Self-labeling techniques for semi-supervised time series classification: an empirical study

González, Mabel; Bergmeir, Christoph; Triguero, Isaac; Rodríguez, Yanet; Benítez, José M.

Authors

Mabel González

Christoph Bergmeir

Yanet Rodríguez

José M. Benítez



Abstract

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.

Citation

González, M., Bergmeir, C., Triguero, I., Rodríguez, Y., & Benítez, J. M. (in press). Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems, https://doi.org/10.1007/s10115-017-1090-9

Journal Article Type Article
Acceptance Date Jul 28, 2017
Online Publication Date Aug 8, 2017
Deposit Date Aug 11, 2017
Publicly Available Date Aug 9, 2018
Journal Knowledge and Information Systems
Print ISSN 0219-1377
Electronic ISSN 0219-3116
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s10115-017-1090-9
Keywords Semi-supervised classification; Self-labeled; Time series classification; Semi-supervised learning; Self-training
Public URL http://eprints.nottingham.ac.uk/id/eprint/44845
Publisher URL https://link.springer.com/article/10.1007%2Fs10115-017-1090-9
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-017-1090-9

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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